diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..9a5e027383a6a094f8af59a141ab498c5f1d23a7
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,2 @@
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..77e24df871b26b98391caccd828b1e3727b54559
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,18 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install minimal system dependencies
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application files
+COPY . .
+
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
diff --git a/Google_Search_tool.py b/Google_Search_tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..f357319d3a28fbba5b47484fdac53f575b16237e
--- /dev/null
+++ b/Google_Search_tool.py
@@ -0,0 +1,41 @@
+import os
+import logging
+from googleapiclient.discovery import build
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class GoogleSearch:
+    """Performs Google Custom Search API queries."""
+    def __init__(self):
+        self.api_key = os.getenv("GOOGLE_API_KEY")
+        self.cse_id = os.getenv("GOOGLE_CSE_ID")
+        self.service = None
+        if self.api_key and self.cse_id:
+            try:
+                self.service = build("customsearch", "v1", developerKey=self.api_key)
+                logging.info("✅ Google Custom Search initialized.")
+            except Exception as e:
+                logging.error(f"❌ CSE init failed: {e}")
+        else:
+            logging.warning("⚠️ GOOGLE_API_KEY or GOOGLE_CSE_ID not set; search disabled.")
+
+    def search(self, queries: list, num_results: int = 1) -> list:
+        if not self.service:
+            return []
+        out = []
+        for q in queries:
+            try:
+                resp = self.service.cse().list(q=q, cx=self.cse_id, num=num_results).execute()
+                items = resp.get("items", [])
+                formatted = [
+                    {"title": it.get("title"), "link": it.get("link"), "snippet": it.get("snippet")}
+                    for it in items
+                ]
+                out.append({"query": q, "results": formatted})
+            except Exception as e:
+                logging.error(f"❌ Search error for '{q}': {e}")
+                out.append({"query": q, "results": []})
+        return out
+
+google_search = GoogleSearch()
diff --git a/README.md b/README.md
index 152c83e3f727caa975abfa7dc16b04e6058073c7..3797697b9342615ceea9b6505e43244930cbf3ca 100644
--- a/README.md
+++ b/README.md
@@ -1,59 +1,14 @@
 ---
-title: HexAI Demo
-emoji: 🔥
-colorFrom: pink
-colorTo: gray
-sdk: static
-pinned: false
-app_build_command: npm run build
-app_file: dist/index.html
-short_description: Demo with limited version
+title: RxGuard Prescription Validator
+emoji: ⚕️
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.36.0
+app_file: app.py
+hf_oauth: true
+hardware:
+  accelerator: T4
+  cpu: 2
+  memory: 16
 ---
-
-# Svelte + TS + Vite
-
-This template should help get you started developing with Svelte and TypeScript in Vite.
-
-## Recommended IDE Setup
-
-[VS Code](https://code.visualstudio.com/) + [Svelte](https://marketplace.visualstudio.com/items?itemName=svelte.svelte-vscode).
-
-## Need an official Svelte framework?
-
-Check out [SvelteKit](https://github.com/sveltejs/kit#readme), which is also powered by Vite. Deploy anywhere with its serverless-first approach and adapt to various platforms, with out of the box support for TypeScript, SCSS, and Less, and easily-added support for mdsvex, GraphQL, PostCSS, Tailwind CSS, and more.
-
-## Technical considerations
-
-**Why use this over SvelteKit?**
-
-- It brings its own routing solution which might not be preferable for some users.
-- It is first and foremost a framework that just happens to use Vite under the hood, not a Vite app.
-
-This template contains as little as possible to get started with Vite + TypeScript + Svelte, while taking into account the developer experience with regards to HMR and intellisense. It demonstrates capabilities on par with the other `create-vite` templates and is a good starting point for beginners dipping their toes into a Vite + Svelte project.
-
-Should you later need the extended capabilities and extensibility provided by SvelteKit, the template has been structured similarly to SvelteKit so that it is easy to migrate.
-
-**Why `global.d.ts` instead of `compilerOptions.types` inside `jsconfig.json` or `tsconfig.json`?**
-
-Setting `compilerOptions.types` shuts out all other types not explicitly listed in the configuration. Using triple-slash references keeps the default TypeScript setting of accepting type information from the entire workspace, while also adding `svelte` and `vite/client` type information.
-
-**Why include `.vscode/extensions.json`?**
-
-Other templates indirectly recommend extensions via the README, but this file allows VS Code to prompt the user to install the recommended extension upon opening the project.
-
-**Why enable `allowJs` in the TS template?**
-
-While `allowJs: false` would indeed prevent the use of `.js` files in the project, it does not prevent the use of JavaScript syntax in `.svelte` files. In addition, it would force `checkJs: false`, bringing the worst of both worlds: not being able to guarantee the entire codebase is TypeScript, and also having worse typechecking for the existing JavaScript. In addition, there are valid use cases in which a mixed codebase may be relevant.
-
-**Why is HMR not preserving my local component state?**
-
-HMR state preservation comes with a number of gotchas! It has been disabled by default in both `svelte-hmr` and `@sveltejs/vite-plugin-svelte` due to its often surprising behavior. You can read the details [here](https://github.com/rixo/svelte-hmr#svelte-hmr).
-
-If you have state that's important to retain within a component, consider creating an external store which would not be replaced by HMR.
-
-```ts
-// store.ts
-// An extremely simple external store
-import { writable } from "svelte/store";
-export default writable(0);
-```
diff --git a/__pycache__/config.cpython-310.pyc b/__pycache__/config.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..facf05a71f762db4a1098cae11e663390bb2fc07
Binary files /dev/null and b/__pycache__/config.cpython-310.pyc differ
diff --git a/__pycache__/config.cpython-39.pyc b/__pycache__/config.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dda04cee96af3fa2c41b5088c8daae2b25e02d51
Binary files /dev/null and b/__pycache__/config.cpython-39.pyc differ
diff --git a/__pycache__/google_search_tool.cpython-310.pyc b/__pycache__/google_search_tool.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..41c59ad7968ff63fa0a3452cb0c3a901ac3bf1aa
Binary files /dev/null and b/__pycache__/google_search_tool.cpython-310.pyc differ
diff --git a/__pycache__/validate_prescription.cpython-310.pyc b/__pycache__/validate_prescription.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..008dc35fb40e9d306183391cf9bcde1a57e2c4dd
Binary files /dev/null and b/__pycache__/validate_prescription.cpython-310.pyc differ
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ad1a32c687eeda008284ed47147aa5bfe654b43
--- /dev/null
+++ b/app.py
@@ -0,0 +1,137 @@
+import os
+import streamlit as st
+from PIL import Image
+from config import (
+    STATIC_DIR,
+    UPLOADS_DIR,
+    HF_TOKEN,
+    GOOGLE_API_KEY,
+    GOOGLE_CSE_ID,
+    GEMINI_API_KEY,
+    DEVICE
+)
+
+# ─── App Configuration ────────────────────────────────────────────────────
+st.set_page_config(
+    page_title="RxGuard Prescription Validator",
+    page_icon="⚕️",
+    layout="wide",
+    menu_items={
+        'Get Help': 'https://github.com/your-repo',
+        'About': "RxGuard v1.0 - Advanced Prescription Validation"
+    }
+)
+
+# ─── Session State ────────────────────────────────────────────────────────
+if "analysis_result" not in st.session_state:
+    st.session_state.analysis_result = None
+if "uploaded_filename" not in st.session_state:
+    st.session_state.uploaded_filename = None
+
+# ─── UI Components ────────────────────────────────────────────────────────
+def show_service_status():
+    """Displays service connectivity status."""
+    cols = st.columns(4)
+    with cols[0]:
+        st.metric("HuggingFace", "✅" if HF_TOKEN else "❌")
+    with cols[1]:
+        st.metric("Google API", "✅" if GOOGLE_API_KEY else "❌")
+    with cols[2]:
+        st.metric("Gemini", "✅" if GEMINI_API_KEY else "❌")
+    with cols[3]:
+        st.metric("Device", DEVICE.upper())
+
+def display_patient_info(info: dict):
+    """Displays patient information in a formatted card."""
+    with st.container(border=True):
+        st.subheader("👤 Patient Details")
+        cols = st.columns(2)
+        with cols[0]:
+            st.markdown(f"**Name:** {info.get('Name', 'Not detected')}")
+            st.markdown(f"**Age:** {info.get('Age', 'N/A')}")
+        with cols[1]:
+            st.markdown(f"**Date:** {info.get('Date', 'N/A')}")
+            st.markdown(f"**Physician:** {info.get('PhysicianName', 'N/A')}")
+
+def display_medications(medications: list):
+    """Displays medication information with verification."""
+    st.subheader("💊 Medications")
+    if not medications:
+        st.warning("No medications detected in prescription")
+        return
+        
+    for med in medications:
+        with st.expander(f"{med.get('drug_raw', 'Unknown Medication')}"):
+            cols = st.columns([1, 2])
+            with cols[0]:
+                st.markdown(f"""
+                **Dosage:** `{med.get('dosage', 'N/A')}`  
+                **Frequency:** `{med.get('frequency', 'N/A')}`
+                """)
+            
+            with cols[1]:
+                if verification := med.get("verification"):
+                    if dosage := verification.get("standard_dosage"):
+                        st.success(f"**Standard Dosage:** {dosage}")
+                    if side_effects := verification.get("side_effects"):
+                        st.warning(f"**Side Effects:** {side_effects}")
+                    if interactions := verification.get("interactions"):
+                        st.error(f"**Interactions:** {interactions}")
+
+# ─── Main Application ─────────────────────────────────────────────────────
+def main():
+    st.title("⚕️ RxGuard Prescription Validator")
+    st.caption("AI-powered prescription verification system")
+    
+    show_service_status()
+    
+    # Only enable upload if required services are available
+    if all([HF_TOKEN, GOOGLE_API_KEY, GEMINI_API_KEY]):
+        uploaded_file = st.file_uploader(
+            "Upload prescription image (PNG/JPG/JPEG):",
+            type=["png", "jpg", "jpeg"],
+            help="Clear image of the prescription"
+        )
+        
+        if uploaded_file and uploaded_file.name != st.session_state.uploaded_filename:
+            with st.status("Analyzing prescription...", expanded=True) as status:
+                try:
+                    # Store the uploaded file
+                    st.session_state.uploaded_filename = uploaded_file.name
+                    file_path = os.path.join(UPLOADS_DIR, uploaded_file.name)
+                    
+                    with open(file_path, "wb") as f:
+                        f.write(uploaded_file.getvalue())
+                    
+                    # Import processing function only when needed
+                    from validate_prescription import extract_prescription_info
+                    st.session_state.analysis_result = extract_prescription_info(file_path)
+                    
+                    status.update(label="Analysis complete!", state="complete", expanded=False)
+                except Exception as e:
+                    st.error(f"Processing failed: {str(e)}")
+                    st.session_state.analysis_result = {"error": str(e)}
+                    status.update(label="Analysis failed", state="error")
+    
+    # Display results if available
+    if st.session_state.analysis_result:
+        result = st.session_state.analysis_result
+        
+        if result.get("error"):
+            st.error(f"❌ Error: {result['error']}")
+        else:
+            tab1, tab2 = st.tabs(["Patient Information", "Medication Details"])
+            
+            with tab1:
+                if uploaded_file:
+                    st.image(uploaded_file, use_column_width=True)
+                display_patient_info(result["info"])
+            
+            with tab2:
+                display_medications(result["info"].get("Medications", []))
+                
+                if st.toggle("Show technical details"):
+                    st.json(result.get("debug_info", {}))
+
+if __name__ == "__main__":
+    main()
diff --git a/apt-packages.txt b/apt-packages.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6b05dfc387ff0e73463dfa959682bf07609450a5
--- /dev/null
+++ b/apt-packages.txt
@@ -0,0 +1,13 @@
+libgl1
+libgl1-mesa-glx
+libglib2.0-0
+tesseract-ocr
+tesseract-ocr-hin
+git
+git-lfs
+curl
+libssl-dev
+libffi-dev
+python3-dev
+build-essential
+libsqlite3-dev
diff --git a/config.py b/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..343c4451a3fc7af328e2367270426c6d8fd8dbbc
--- /dev/null
+++ b/config.py
@@ -0,0 +1,37 @@
+import os
+import torch
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# ─── Directory Configuration ────────────────────────────────────────────────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+STATIC_DIR = os.path.join(BASE_DIR, 'static')
+os.makedirs(STATIC_DIR, exist_ok=True)
+
+# ─── API Secrets ────────────────────────────────────────────────────────────
+HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")  # For Hugging Face models
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")  # For Gemini and Custom Search
+GOOGLE_CSE_ID = os.getenv("GOOGLE_CSE_ID")  # For medication verification
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")  # Alternative Gemini auth
+HALODOC_API_KEY = os.getenv("HALODOC_API_KEY")  # Future integration
+
+# ─── Model Configuration ────────────────────────────────────────────────────
+HF_MODELS = {
+    "donut": "naver-clova-ix/donut-base-finetuned-cord-v2",
+    "phi3": "microsoft/phi-3-mini-4k-instruct",
+}
+GEMINI_MODEL_NAME = "gemini-1.5-flash"  # Balanced for speed and accuracy
+
+# ─── Processing Parameters ─────────────────────────────────────────────────
+LEV_THRESH = 0.75  # Levenshtein similarity threshold
+SIG_THRESH = 0.65  # Signature verification threshold
+
+# ─── File Paths ───────────────────────────────────────────────────────────
+DB_PATH = os.path.join(STATIC_DIR, "rxguard.db")
+UPLOADS_DIR = os.path.join(STATIC_DIR, "uploads")
+os.makedirs(UPLOADS_DIR, exist_ok=True)
+
+# ─── Hardware Configuration ────────────────────────────────────────────────
+DEVICE = "cpu"  # Force CPU for Hugging Face Spaces compatibility
+USE_GPU = False
diff --git a/data/interactions.csv b/data/interactions.csv
new file mode 100644
index 0000000000000000000000000000000000000000..815ad5af3124eaa748c3d8e2dfa602097405abac
--- /dev/null
+++ b/data/interactions.csv
@@ -0,0 +1,3 @@
+cui1,cui2,severity,advice
+1191,6801,moderate,"Monitor blood glucose closely; aspirin may enhance the hypoglycemic effect of metformin."
+4241,1113,moderate,"Ascorbic acid can increase the absorption of iron. While often beneficial, monitor for iron overload in susceptible patients."
diff --git a/data/rxnorm.db b/data/rxnorm.db
new file mode 100644
index 0000000000000000000000000000000000000000..8dade48909ab53de6764c55a329191edb5f58cf5
Binary files /dev/null and b/data/rxnorm.db differ
diff --git a/data/rxnorm_names.csv b/data/rxnorm_names.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ae175da09cf28ff9d578aedd38a0656bed86b60c
--- /dev/null
+++ b/data/rxnorm_names.csv
@@ -0,0 +1,13 @@
+name,cui
+Aspirin,1191
+Acetaminophen,161
+Amoxicillin,723
+Metformin,6801
+Lisinopril,29046
+Ferrous Sulfate,4241
+Ascorbic Acid,1113
+Calcium Carbonate / Vitamin D3,12345
+Clonazepam,2623
+Meganeuron,54321
+Creatine,9876
+Salbutamol,9648
diff --git a/drug_interaction_detection/__pycache__/interaction_checker.cpython-310.pyc b/drug_interaction_detection/__pycache__/interaction_checker.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..23488f8e8c168e82b9b2a111b51d7cc0e44c4488
Binary files /dev/null and b/drug_interaction_detection/__pycache__/interaction_checker.cpython-310.pyc differ
diff --git a/drug_interaction_detection/interaction_checker.py b/drug_interaction_detection/interaction_checker.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2811c30b90979b4624356423271a102c44db5ba
--- /dev/null
+++ b/drug_interaction_detection/interaction_checker.py
@@ -0,0 +1,34 @@
+# drug_interaction_detection/interaction_checker.py
+import sqlite3
+from config import DB_PATH
+
+class InteractionChecker:
+    def __init__(self, db_path: str = DB_PATH):
+        self.conn = sqlite3.connect(db_path)
+        self.conn.row_factory = sqlite3.Row
+
+    def find(self, cuis: list[str]) -> list[dict]:
+        if len(cuis) < 2: return []
+        
+        # Create a list of all unique pairs
+        pairs = []
+        for i in range(len(cuis)):
+            for j in range(i + 1, len(cuis)):
+                # Ensure consistent order for querying (e.g., smaller CUI first)
+                pair = tuple(sorted((cuis[i], cuis[j])))
+                pairs.append(pair)
+        
+        if not pairs: return []
+
+        # Query for all pairs at once
+        placeholders = ", ".join(["(?, ?)"] * len(pairs))
+        flat_params = [item for pair in pairs for item in pair]
+        
+        query = f"""
+            SELECT cui1, cui2, severity, advice 
+            FROM interactions 
+            WHERE (cui1, cui2) IN ({placeholders})
+        """
+        
+        rows = self.conn.execute(query, flat_params).fetchall()
+        return [{"pair": (r["cui1"], r["cui2"]), "severity": r["severity"], "advice": r["advice"]} for r in rows]
diff --git a/earnest-trilogy-465710-e7-6cc7bbbddb97.json b/earnest-trilogy-465710-e7-6cc7bbbddb97.json
new file mode 100644
index 0000000000000000000000000000000000000000..c6c65f131b35215f28287dd21e763e114032755f
--- /dev/null
+++ b/earnest-trilogy-465710-e7-6cc7bbbddb97.json
@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "earnest-trilogy-465710-e7",
+  "private_key_id": "6cc7bbbddb970f16876e3f4120ff18be704f5ee5",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nJUzmpT+C4PQCHMepVFoaLw==\n-----END PRIVATE KEY-----\n",
+  "client_email": "aihackathon@earnest-trilogy-465710-e7.iam.gserviceaccount.com",
+  "client_id": "102524806684057807383",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/aihackathon%40earnest-trilogy-465710-e7.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}
diff --git a/index.html b/index.html
deleted file mode 100644
index b6c5f0afafd6f2e009ed3d2c5de7351bcf7fb12d..0000000000000000000000000000000000000000
--- a/index.html
+++ /dev/null
@@ -1,13 +0,0 @@
-<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Vite + Svelte + TS</title>
-  </head>
-  <body>
-    <div id="app"></div>
-    <script type="module" src="/src/main.ts"></script>
-  </body>
-</html>
diff --git a/models/signature_sia.tflite b/models/signature_sia.tflite
new file mode 100644
index 0000000000000000000000000000000000000000..d9fa2bc3485c6b9dc476c2e5a80b4497f6880700
--- /dev/null
+++ b/models/signature_sia.tflite
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ba80e2f5c688e9fd9fc766a85f266d0a47201def65265ef294168546a2814de
+size 804888
diff --git a/package.json b/package.json
deleted file mode 100644
index 9013b8ca459416df8386e8607bf202977c5442de..0000000000000000000000000000000000000000
--- a/package.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-  "name": "svelte",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview",
-    "check": "svelte-check --tsconfig ./tsconfig.app.json && tsc -p tsconfig.node.json"
-  },
-  "devDependencies": {
-    "@sveltejs/vite-plugin-svelte": "^5.0.3",
-    "@tsconfig/svelte": "^5.0.4",
-    "svelte": "^5.28.1",
-    "svelte-check": "^4.1.6",
-    "typescript": "~5.8.3",
-    "vite": "^6.3.5"
-  }
-}
diff --git a/prescription_validation/__pycache__/fuzzy_match.cpython-310.pyc b/prescription_validation/__pycache__/fuzzy_match.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..35aaef85b25ee925fe3c78c724014efa37d64a3e
Binary files /dev/null and b/prescription_validation/__pycache__/fuzzy_match.cpython-310.pyc differ
diff --git a/prescription_validation/fuzzy_match.py b/prescription_validation/fuzzy_match.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e3ca2fa5d9bf2bea8c56426d867a5debae49bc5
--- /dev/null
+++ b/prescription_validation/fuzzy_match.py
@@ -0,0 +1,44 @@
+# prescription_validation/fuzzy_match.py
+import sqlite3
+import re
+from rapidfuzz.distance import Levenshtein
+from config import DB_PATH, LEV_THRESH
+
+class RxLookup:
+    def __init__(self, db_path: str = DB_PATH):
+        self.conn = sqlite3.connect(db_path)
+        self.conn.row_factory = sqlite3.Row
+        self.drugs = self.conn.execute("SELECT name, cui FROM drugs").fetchall()
+
+    def _clean_token(self, token: str) -> str:
+        """Removes dosage, form factor, and non-alpha characters."""
+        cleaned = token.lower()
+        cleaned = re.sub(r'(\d+)\s*(mg|ml|mcg|tab|cap|#)', '', cleaned)
+        cleaned = re.sub(r'[^a-z]', '', cleaned)
+        return cleaned
+
+    def match(self, token: str) -> tuple[str | None, str | None]:
+        if not token:
+            return (None, None)
+            
+        cleaned_token = self._clean_token(token)
+        if not cleaned_token:
+            return (None, None)
+
+        best_match = None
+        min_distance = float('inf')
+
+        for row in self.drugs:
+            name, cui = row["name"], row["cui"]
+            cleaned_db_name = self._clean_token(name)
+            
+            distance = Levenshtein.distance(cleaned_token, cleaned_db_name)
+            
+            if distance < min_distance:
+                min_distance = distance
+                best_match = (name, cui)
+
+        if best_match and min_distance / len(cleaned_token) < LEV_THRESH:
+            return best_match
+
+        return (None, None)
diff --git a/prescription_validation/zone_detector.py b/prescription_validation/zone_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c1b69adb7cd2a3bfa329a782793bbf79db95f24
--- /dev/null
+++ b/prescription_validation/zone_detector.py
@@ -0,0 +1,27 @@
+# prescription_validation/zone_detector.py
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+class ZoneDetector:
+    def __init__(self, model_path: str = "models/signature_model.pt"):
+        try:
+            self.model = YOLO(model_path)
+            print(f"✅ Loaded local YOLO model from '{model_path}'")
+        except Exception as e:
+            self.model = None
+            print(f"❌ Failed to load local YOLO model from '{model_path}'. Please ensure the model file exists.")
+
+    def detect(self, img: np.ndarray) -> list[dict]:
+        if not self.model: return []
+        rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        results = self.model(rgb_img, verbose=False)
+        detections = []
+        for box in results[0].boxes:
+            x1, y1, x2, y2 = box.xyxy[0].tolist()
+            class_id = int(box.cls[0])
+            detections.append({
+                "label": self.model.names[class_id],
+                "bbox": tuple(map(int, [x1, y1, x2, y2])),
+            })
+        return detections
diff --git a/public/vite.svg b/public/vite.svg
deleted file mode 100644
index e7b8dfb1b2a60bd50538bec9f876511b9cac21e3..0000000000000000000000000000000000000000
--- a/public/vite.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..65260cecff634926fac4fe3ad07852200fc38098
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,23 @@
+# Core
+streamlit==1.36.0
+python-dotenv==1.0.1
+
+# AI & Vision
+# Using Google's recommended versions for Gemini and Vision
+google-generativeai==0.7.1
+google-cloud-vision==3.7.3
+torch==2.3.1
+pillow==10.3.0
+transformers==4.41.0
+
+# OCR
+paddleocr==2.7.3
+# Using the CPU version of paddlepaddle for broader compatibility on HF Spaces
+paddlepaddle==2.6.1
+
+# Utils
+numpy==1.26.4
+requests==2.32.3
+opencv-python-headless==4.10.0.84
+scikit-image==0.22.0
+pytz==2024.1
diff --git a/scripts/build_interactions_db.py b/scripts/build_interactions_db.py
new file mode 100644
index 0000000000000000000000000000000000000000..c998337b601e5d6ad1384ced042eeb093615f8b4
--- /dev/null
+++ b/scripts/build_interactions_db.py
@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+import sqlite3, csv, argparse
+import sys, os
+sys.path.append(os.getcwd())
+from config import DB_PATH, INTERACTIONS_CSV
+
+def build_db(db_path: str, csv_path: str):
+    conn = sqlite3.connect(db_path)
+    c = conn.cursor()
+    c.execute("DROP TABLE IF EXISTS interactions")
+    c.execute("CREATE TABLE interactions(cui1 TEXT, cui2 TEXT, severity TEXT, advice TEXT, UNIQUE(cui1, cui2))")
+    
+    with open(csv_path, newline='', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            # Ensure consistent order to avoid duplicate entries like (A,B) and (B,A)
+            cui1, cui2 = sorted((row['cui1'], row['cui2']))
+            c.execute(
+                "INSERT OR IGNORE INTO interactions(cui1, cui2, severity, advice) VALUES(?,?,?,?)",
+                (cui1, cui2, row['severity'], row['advice'])
+            )
+    conn.commit()
+    conn.close()
+
+if __name__ == "__main__":
+    build_db(DB_PATH, INTERACTIONS_CSV)
+    print(f"✅ Interactions DB built at {DB_PATH}")
diff --git a/scripts/build_rxnorm_db.py b/scripts/build_rxnorm_db.py
new file mode 100644
index 0000000000000000000000000000000000000000..fda5823aade24623c0a86d8bae700ee9dab00389
--- /dev/null
+++ b/scripts/build_rxnorm_db.py
@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+import sqlite3, csv, argparse
+from metaphone import doublemetaphone
+import sys, os
+sys.path.append(os.getcwd())
+from config import DB_PATH, RXNORM_CSV
+
+def build_db(db_path: str, csv_path: str):
+    conn = sqlite3.connect(db_path)
+    c = conn.cursor()
+    c.execute("DROP TABLE IF EXISTS drugs")
+    c.execute("DROP TABLE IF EXISTS metaphone")
+    c.execute("CREATE TABLE drugs(name TEXT PRIMARY KEY, cui TEXT)")
+    c.execute("CREATE TABLE metaphone(meta TEXT, name TEXT, cui TEXT)")
+
+    with open(csv_path, newline='', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            nm, cui = row['name'], row['cui']
+            c.execute("INSERT OR IGNORE INTO drugs VALUES(?,?)", (nm, cui))
+            meta = doublemetaphone(nm)[0]
+            c.execute("INSERT INTO metaphone VALUES(?,?,?)", (meta, nm, cui))
+    conn.commit()
+    conn.close()
+
+if __name__ == "__main__":
+    build_db(DB_PATH, RXNORM_CSV)
+    print(f"✅ RxNorm DB built at {DB_PATH}")
diff --git a/scripts/create_dummy_signature_model.py b/scripts/create_dummy_signature_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..06c4305c42693f9e5625dd44401a4461a2b25694
--- /dev/null
+++ b/scripts/create_dummy_signature_model.py
@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+import os
+import tensorflow as tf
+from tensorflow.keras.layers import Input, Flatten, Dense, Subtract
+from tensorflow.keras.models import Model
+import sys
+sys.path.append(os.getcwd())
+from config import SIA_MODEL_PATH
+
+def build_dummy_siamese():
+    inp_a = Input(shape=(224, 224, 1), name="img_a")
+    inp_b = Input(shape=(224, 224, 1), name="img_b")
+    encoder_input = Input(shape=(224, 224, 1))
+    x = Flatten()(encoder_input)
+    x = Dense(16, activation="relu")(x)
+    encoder = Model(encoder_input, x)
+    encoded_a = encoder(inp_a)
+    encoded_b = encoder(inp_b)
+    distance = Subtract()([encoded_a, encoded_b])
+    model = Model(inputs=[inp_a, inp_b], outputs=distance)
+    return model
+
+if __name__ == "__main__":
+    print("Building and converting dummy Siamese model...")
+    model = build_dummy_siamese()
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    tflite_model = converter.convert()
+    os.makedirs(os.path.dirname(SIA_MODEL_PATH), exist_ok=True)
+    with open(SIA_MODEL_PATH, "wb") as f:
+        f.write(tflite_model)
+    print(f"✅ Dummy TFLite signature model saved to '{SIA_MODEL_PATH}'")
diff --git a/signature_verification/__pycache__/signature_detector.cpython-310.pyc b/signature_verification/__pycache__/signature_detector.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..748c4c72e8cc998ca1165e3b5435b2a64365acbe
Binary files /dev/null and b/signature_verification/__pycache__/signature_detector.cpython-310.pyc differ
diff --git a/signature_verification/__pycache__/signature_generator.cpython-310.pyc b/signature_verification/__pycache__/signature_generator.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4fe60963b121626f9e55b0db8a21eb62eb57585a
Binary files /dev/null and b/signature_verification/__pycache__/signature_generator.cpython-310.pyc differ
diff --git a/signature_verification/__pycache__/signature_siamese.cpython-310.pyc b/signature_verification/__pycache__/signature_siamese.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3930e68ce53f1aebf91c7c6d91a13825230b135e
Binary files /dev/null and b/signature_verification/__pycache__/signature_siamese.cpython-310.pyc differ
diff --git a/signature_verification/signature_detector.py b/signature_verification/signature_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a70ee57de4f18d2e8087344cd359432d920176f
--- /dev/null
+++ b/signature_verification/signature_detector.py
@@ -0,0 +1,40 @@
+# signature_verification/signature_detector.py
+import cv2
+import numpy as np
+
+class SignatureDetector:
+    """
+    Detects and crops a signature from a prescription image using refined OpenCV contour analysis.
+    This method is fast, offline, and does not require a pre-trained model.
+    """
+    def crop(self, img: np.ndarray) -> np.ndarray | None:
+        if img is None: return None
+
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+        _, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours: return None
+
+        height, width = img.shape[:2]
+        significant_contours = []
+        for cnt in contours:
+            x, y, w, h = cv2.boundingRect(cnt)
+            # Filter for contours that are reasonably large and in the bottom 70% of the page
+            if w > 50 and h > 15 and y > height * 0.3:
+                if w < width * 0.8 and h < height * 0.4:
+                    significant_contours.append(cnt)
+
+        if not significant_contours: return None
+
+        largest_contour = max(significant_contours, key=cv2.contourArea)
+        x, y, w, h = cv2.boundingRect(largest_contour)
+        
+        padding = 15
+        x1 = max(0, x - padding)
+        y1 = max(0, y - padding)
+        x2 = min(width, x + w + padding)
+        y2 = min(height, y + h + padding)
+        
+        return img[y1:y2, x1:x2]
diff --git a/signature_verification/signature_generator.py b/signature_verification/signature_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fa10070cec7fe3df3f9ed3aed2e71034d65ee84
--- /dev/null
+++ b/signature_verification/signature_generator.py
@@ -0,0 +1,50 @@
+import os
+import logging
+import numpy as np
+import cv2
+from dotenv import load_dotenv
+from huggingface_hub import InferenceClient, HfFolder
+from PIL import Image
+
+load_dotenv()
+
+# Acquire token
+hf_token = os.getenv("HUGGING_FACE_HUB_TOKEN") or HfFolder.get_token()
+if not hf_token:
+    print("❌ No Hugging Face token found. Signature generation will fail.")
+    CLIENT = None
+else:
+    print("✅ Using Hugging Face token for signature generation.")
+    CLIENT = InferenceClient(token=hf_token)
+
+MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
+
+def generate_signatures(name: str, num_variations: int = 3) -> list:
+    """Generates multiple signature variations for a given name."""
+    if CLIENT is None:
+        return []
+
+    prompts = [
+        f"A clean, elegant, handwritten signature of the name '{name}' on a plain white background. Cursive, professional.",
+        f"Calligraphy signature of '{name}'. Black ink on white paper. Minimalist, artistic.",
+        f"A doctor's signature for '{name}'. Quick, scribbled, but legible. Official-looking script."
+    ]
+    images = []
+    for i in range(num_variations):
+        prompt = prompts[i % len(prompts)]
+        logging.info(f"Generating signature variation {i+1} for '{name}'…")
+        try:
+            pil_img = CLIENT.text_to_image(
+                prompt,
+                model=MODEL_ID,
+                negative_prompt=(
+                    "photograph, text, multiple signatures, watermark, blurry, colorful, background"
+                ),
+                guidance_scale=8.0
+            )
+            np_img = np.array(pil_img)
+            cv_img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
+            images.append(cv_img)
+        except Exception as e:
+            logging.error(f"❌ Signature generation failed: {e}")
+    return images
diff --git a/signature_verification/signature_siamese.py b/signature_verification/signature_siamese.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcc4ec564b895113d4ac65555f1e9b84e52bbe09
--- /dev/null
+++ b/signature_verification/signature_siamese.py
@@ -0,0 +1,34 @@
+# signature_verification/signature_siamese.py
+import cv2
+import numpy as np
+import tensorflow as tf
+from config import SIA_MODEL_PATH, SIG_THRESH
+
+class SignatureSiamese:
+    def __init__(self, model_path: str = SIA_MODEL_PATH):
+        try:
+            self.interp = tf.lite.Interpreter(model_path=model_path)
+            self.interp.allocate_tensors()
+            ids = self.interp.get_input_details()
+            self.in1, self.in2 = ids[0]['index'], ids[1]['index']
+            self.out_idx = self.interp.get_output_details()[0]['index']
+            print(f"✅ Loaded TFLite Siamese model from {model_path}")
+        except Exception as e:
+            self.interp = None
+            print(f"❌ Failed to load Siamese model from {model_path}: {e}")
+
+    def _prep(self, img: np.ndarray) -> np.ndarray:
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if img.ndim == 3 else img
+        r = cv2.resize(gray, (224, 224))
+        return r.reshape(1, 224, 224, 1).astype(np.float32) / 255.0
+
+    def verify(self, img1: np.ndarray, img2: np.ndarray) -> tuple[bool, float]:
+        if not self.interp:
+            return False, float('inf')
+        inp1, inp2 = self._prep(img1), self._prep(img2)
+        self.interp.set_tensor(self.in1, inp1)
+        self.interp.set_tensor(self.in2, inp2)
+        self.interp.invoke()
+        diff = self.interp.get_tensor(self.out_idx)
+        dist = np.linalg.norm(diff)
+        return dist < SIG_THRESH, dist
diff --git a/src/App.svelte b/src/App.svelte
deleted file mode 100644
index f75b68aa25d9c715d2c7fa808b24516bf89e0f11..0000000000000000000000000000000000000000
--- a/src/App.svelte
+++ /dev/null
@@ -1,47 +0,0 @@
-<script lang="ts">
-  import svelteLogo from './assets/svelte.svg'
-  import viteLogo from '/vite.svg'
-  import Counter from './lib/Counter.svelte'
-</script>
-
-<main>
-  <div>
-    <a href="https://vite.dev" target="_blank" rel="noreferrer">
-      <img src={viteLogo} class="logo" alt="Vite Logo" />
-    </a>
-    <a href="https://svelte.dev" target="_blank" rel="noreferrer">
-      <img src={svelteLogo} class="logo svelte" alt="Svelte Logo" />
-    </a>
-  </div>
-  <h1>Vite + Svelte</h1>
-
-  <div class="card">
-    <Counter />
-  </div>
-
-  <p>
-    Check out <a href="https://github.com/sveltejs/kit#readme" target="_blank" rel="noreferrer">SvelteKit</a>, the official Svelte app framework powered by Vite!
-  </p>
-
-  <p class="read-the-docs">
-    Click on the Vite and Svelte logos to learn more
-  </p>
-</main>
-
-<style>
-  .logo {
-    height: 6em;
-    padding: 1.5em;
-    will-change: filter;
-    transition: filter 300ms;
-  }
-  .logo:hover {
-    filter: drop-shadow(0 0 2em #646cffaa);
-  }
-  .logo.svelte:hover {
-    filter: drop-shadow(0 0 2em #ff3e00aa);
-  }
-  .read-the-docs {
-    color: #888;
-  }
-</style>
diff --git a/src/app.css b/src/app.css
deleted file mode 100644
index 61ba367832ad77dd81568fe7b66af119713fb56f..0000000000000000000000000000000000000000
--- a/src/app.css
+++ /dev/null
@@ -1,79 +0,0 @@
-:root {
-  font-family: system-ui, Avenir, Helvetica, Arial, sans-serif;
-  line-height: 1.5;
-  font-weight: 400;
-
-  color-scheme: light dark;
-  color: rgba(255, 255, 255, 0.87);
-  background-color: #242424;
-
-  font-synthesis: none;
-  text-rendering: optimizeLegibility;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-
-a {
-  font-weight: 500;
-  color: #646cff;
-  text-decoration: inherit;
-}
-a:hover {
-  color: #535bf2;
-}
-
-body {
-  margin: 0;
-  display: flex;
-  place-items: center;
-  min-width: 320px;
-  min-height: 100vh;
-}
-
-h1 {
-  font-size: 3.2em;
-  line-height: 1.1;
-}
-
-.card {
-  padding: 2em;
-}
-
-#app {
-  max-width: 1280px;
-  margin: 0 auto;
-  padding: 2rem;
-  text-align: center;
-}
-
-button {
-  border-radius: 8px;
-  border: 1px solid transparent;
-  padding: 0.6em 1.2em;
-  font-size: 1em;
-  font-weight: 500;
-  font-family: inherit;
-  background-color: #1a1a1a;
-  cursor: pointer;
-  transition: border-color 0.25s;
-}
-button:hover {
-  border-color: #646cff;
-}
-button:focus,
-button:focus-visible {
-  outline: 4px auto -webkit-focus-ring-color;
-}
-
-@media (prefers-color-scheme: light) {
-  :root {
-    color: #213547;
-    background-color: #ffffff;
-  }
-  a:hover {
-    color: #747bff;
-  }
-  button {
-    background-color: #f9f9f9;
-  }
-}
diff --git a/src/assets/svelte.svg b/src/assets/svelte.svg
deleted file mode 100644
index c5e08481f8aede5a6cc897b0f034e806a8617d7d..0000000000000000000000000000000000000000
--- a/src/assets/svelte.svg
+++ /dev/null
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\ No newline at end of file
diff --git a/src/lib/Counter.svelte b/src/lib/Counter.svelte
deleted file mode 100644
index 37d75ce76f59c812d02ac54c15ce89c94ea7a08a..0000000000000000000000000000000000000000
--- a/src/lib/Counter.svelte
+++ /dev/null
@@ -1,10 +0,0 @@
-<script lang="ts">
-  let count: number = $state(0)
-  const increment = () => {
-    count += 1
-  }
-</script>
-
-<button onclick={increment}>
-  count is {count}
-</button>
diff --git a/src/main.ts b/src/main.ts
deleted file mode 100644
index 664a057afa0f4922490083dffada64a6a626bedf..0000000000000000000000000000000000000000
--- a/src/main.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-import { mount } from 'svelte'
-import './app.css'
-import App from './App.svelte'
-
-const app = mount(App, {
-  target: document.getElementById('app')!,
-})
-
-export default app
diff --git a/src/vite-env.d.ts b/src/vite-env.d.ts
deleted file mode 100644
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--- a/src/vite-env.d.ts
+++ /dev/null
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-/// <reference types="svelte" />
-/// <reference types="vite/client" />
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+version https://git-lfs.github.com/spec/v1
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+version https://git-lfs.github.com/spec/v1
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+size 18478
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f93994163fa2ad4a9c3faaab651c602e3e7c76bb956cc8e10d4531c185f022df
+size 18478
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+++ b/static/uploads/sig_dcd130973735495d8b02e9a825c45247_vio-4.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f93994163fa2ad4a9c3faaab651c602e3e7c76bb956cc8e10d4531c185f022df
+size 18478
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+version https://git-lfs.github.com/spec/v1
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+size 18478
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+version https://git-lfs.github.com/spec/v1
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+size 18478
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+version https://git-lfs.github.com/spec/v1
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+version https://git-lfs.github.com/spec/v1
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diff --git a/svelte.config.js b/svelte.config.js
deleted file mode 100644
index b0683fd24d70abb7eeaeef8e39e3a12b4e74775a..0000000000000000000000000000000000000000
--- a/svelte.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { vitePreprocess } from '@sveltejs/vite-plugin-svelte'
-
-export default {
-  // Consult https://svelte.dev/docs#compile-time-svelte-preprocess
-  // for more information about preprocessors
-  preprocess: vitePreprocess(),
-}
diff --git a/tsconfig.app.json b/tsconfig.app.json
deleted file mode 100644
index 55a2f9b6548ebf5b6d8fbe87c2cd9fdf8c2492a9..0000000000000000000000000000000000000000
--- a/tsconfig.app.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-  "extends": "@tsconfig/svelte/tsconfig.json",
-  "compilerOptions": {
-    "target": "ESNext",
-    "useDefineForClassFields": true,
-    "module": "ESNext",
-    "resolveJsonModule": true,
-    /**
-     * Typecheck JS in `.svelte` and `.js` files by default.
-     * Disable checkJs if you'd like to use dynamic types in JS.
-     * Note that setting allowJs false does not prevent the use
-     * of JS in `.svelte` files.
-     */
-    "allowJs": true,
-    "checkJs": true,
-    "isolatedModules": true,
-    "moduleDetection": "force"
-  },
-  "include": ["src/**/*.ts", "src/**/*.js", "src/**/*.svelte"]
-}
diff --git a/tsconfig.json b/tsconfig.json
deleted file mode 100644
index 1ffef600d959ec9e396d5a260bd3f5b927b2cef8..0000000000000000000000000000000000000000
--- a/tsconfig.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "files": [],
-  "references": [
-    { "path": "./tsconfig.app.json" },
-    { "path": "./tsconfig.node.json" }
-  ]
-}
diff --git a/tsconfig.node.json b/tsconfig.node.json
deleted file mode 100644
index 9728af2d81af0efc7985ffc705287be4bad10ba4..0000000000000000000000000000000000000000
--- a/tsconfig.node.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
-  "compilerOptions": {
-    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
-    "target": "ES2022",
-    "lib": ["ES2023"],
-    "module": "ESNext",
-    "skipLibCheck": true,
-
-    /* Bundler mode */
-    "moduleResolution": "bundler",
-    "allowImportingTsExtensions": true,
-    "verbatimModuleSyntax": true,
-    "moduleDetection": "force",
-    "noEmit": true,
-
-    /* Linting */
-    "strict": true,
-    "noUnusedLocals": true,
-    "noUnusedParameters": true,
-    "erasableSyntaxOnly": true,
-    "noFallthroughCasesInSwitch": true,
-    "noUncheckedSideEffectImports": true
-  },
-  "include": ["vite.config.ts"]
-}
diff --git a/validate_prescription.py b/validate_prescription.py
new file mode 100644
index 0000000000000000000000000000000000000000..7de38a2f4ecaf4dc6005a8b46c2d40c562aebfb4
--- /dev/null
+++ b/validate_prescription.py
@@ -0,0 +1,336 @@
+import os
+import re
+import json
+import logging
+import time
+import numpy as np
+import tempfile
+import sqlite3
+import torch
+import io
+from typing import Dict, Any, List, Optional
+from PIL import Image
+from dotenv import load_dotenv
+from googleapiclient.discovery import build
+
+# Suppress verbose backend logs
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+load_dotenv()
+
+try:
+    from transformers import pipeline, DonutProcessor, VisionEncoderDecoderModel, AutoTokenizer
+    from huggingface_hub import login
+    import google.generativeai as genai
+except ImportError as e:
+    logging.error(f"Failed to import critical AI libraries: {e}")
+    raise
+
+from config import (
+    DB_PATH,
+    HF_TOKEN,
+    HF_MODELS,
+    GOOGLE_API_KEY,
+    GOOGLE_APPLICATION_CREDENTIALS,
+    GEMINI_MODEL_NAME,
+    DEVICE,
+    USE_GPU,
+    GOOGLE_CSE_ID,
+)
+
+# ─── Configure Logging & Auth ────────────────────────────────────────────────
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+if HF_TOKEN:
+    login(token=HF_TOKEN)
+
+# ─── Singleton Holders for AI Models ─────────────────────────────────────────
+_MODELS = {}
+_TEMP_CRED_FILE = None
+
+class GoogleSearch:
+    """Performs Google Custom Search API queries."""
+    def __init__(self):
+        self.api_key = GOOGLE_API_KEY
+        self.cse_id = GOOGLE_CSE_ID
+        self.service = None
+        if self.api_key and self.cse_id:
+            try:
+                self.service = build("customsearch", "v1", developerKey=self.api_key)
+                logging.info("Google Custom Search initialized.")
+            except Exception as e:
+                logging.error(f"CSE init failed: {e}")
+        else:
+            logging.warning("GOOGLE_API_KEY or GOOGLE_CSE_ID not set; search disabled.")
+
+    def search(self, queries: list, num_results: int = 1) -> list:
+        if not self.service:
+            return []
+        out = []
+        for q in queries:
+            try:
+                resp = self.service.cse().list(q=q, cx=self.cse_id, num=num_results).execute()
+                items = resp.get("items", [])
+                formatted = [
+                    {"title": it.get("title"), "link": it.get("link"), "snippet": it.get("snippet")}
+                    for it in items
+                ]
+                out.append({"query": q, "results": formatted})
+            except Exception as e:
+                logging.error(f"Search error for '{q}': {e}")
+                out.append({"query": q, "results": []})
+        return out
+
+# Initialize Google Search globally
+google_search = GoogleSearch()
+
+def get_model(name: str):
+    """Loads and caches AI models to avoid reloading."""
+    if name not in _MODELS:
+        model_id = HF_MODELS.get(name)
+        if not model_id:
+            return None
+
+        logging.info(f"Loading model '{name}' ({model_id}) to device '{DEVICE}'...")
+        try:
+            quantization_config = {"load_in_8bit": True} if USE_GPU else {}
+            if name == "donut":
+                processor = DonutProcessor.from_pretrained(model_id)
+                model = VisionEncoderDecoderModel.from_pretrained(model_id, **quantization_config)
+                _MODELS[name] = {"model": model, "processor": processor}
+            elif name == "phi3":
+                model = pipeline(
+                    "text-generation",
+                    model=model_id,
+                    torch_dtype=torch.bfloat16,
+                    trust_remote_code=True,
+                    **quantization_config
+                )
+                _MODELS[name] = {"model": model}
+            logging.info(f"Model '{name}' loaded successfully.")
+        except Exception as e:
+            logging.error(f"Failed to load model '{name}': {e}", exc_info=True)
+            _MODELS[name] = None
+
+    return _MODELS.get(name)
+
+def get_gemini_client():
+    """Initializes and returns the Gemini client."""
+    global _TEMP_CRED_FILE
+
+    if "gemini" not in _MODELS:
+        # Write out credentials file if needed
+        if creds_json_str := GOOGLE_APPLICATION_CREDENTIALS:
+            if not _TEMP_CRED_FILE or not os.path.exists(_TEMP_CRED_FILE):
+                with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".json") as tfp:
+                    tfp.write(creds_json_str)
+                    _TEMP_CRED_FILE = tfp.name
+                os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = _TEMP_CRED_FILE
+
+        try:
+            genai.configure(api_key=GOOGLE_API_KEY)
+            _MODELS["gemini"] = genai.GenerativeModel(GEMINI_MODEL_NAME)
+        except Exception as e:
+            logging.error(f"Gemini init failed: {e}")
+            _MODELS["gemini"] = None
+
+    return _MODELS.get("gemini")
+
+def verify_medication_with_google(medication_name: str) -> Dict[str, Any]:
+    """Verifies medication details using Google Search API."""
+    if not medication_name:
+        return {"error": "No medication name provided"}
+    
+    queries = [
+        f"standard dosage for {medication_name}",
+        f"side effects of {medication_name}",
+        f"drug interactions with {medication_name}"
+    ]
+    
+    search_results = google_search.search(queries, num_results=2)
+    return {
+        "medication": medication_name,
+        "verification_results": search_results
+    }
+
+def step1_run_donut(image: Image.Image) -> Dict[str, Any]:
+    donut_components = get_model("donut")
+    if not donut_components:
+        return {"error": "Donut model not available."}
+
+    model = donut_components["model"].to(DEVICE)
+    processor = donut_components["processor"]
+
+    task_prompt = "<s_cord-v2>"
+    decoder_input_ids = processor.tokenizer(
+        task_prompt, add_special_tokens=False, return_tensors="pt"
+    ).input_ids.to(DEVICE)
+    pixel_values = processor(image, return_tensors="pt").pixel_values.to(DEVICE)
+
+    outputs = model.generate(
+        pixel_values,
+        decoder_input_ids=decoder_input_ids,
+        max_length=model.decoder.config.max_position_embeddings,
+        early_stopping=True,
+        use_cache=True,
+        num_beams=1,
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+    sequence = (
+        processor.batch_decode(outputs.sequences)[0]
+        .replace(processor.tokenizer.eos_token, "")
+        .replace(processor.tokenizer.pad_token, "")
+    )
+    return processor.token2json(sequence)
+
+def step2_run_phi3(medication_text: str) -> str:
+    phi3_components = get_model("phi3")
+    if not phi3_components:
+        return medication_text
+
+    pipe = phi3_components["model"]
+    prompt = (
+        f"Normalize the following prescription medication line into its components "
+        f"(drug, dosage, frequency). Raw text: '{medication_text}'"
+    )
+    outputs = pipe(prompt, max_new_tokens=100, do_sample=False)
+    return outputs[0]["generated_text"].split("Normalized:")[-1].strip()
+
+def step3_run_gemini_resolver(
+    image: Image.Image, donut_result: Dict[str, Any], phi3_results: List[str]
+) -> Dict[str, Any]:
+    gemini_client = get_gemini_client()
+    if not gemini_client:
+        return {"error": "Gemini resolver not available."}
+
+    prompt = f"""
+You are an expert pharmacist's assistant whose sole objective is to reconcile and verify prescription details by cross-referencing multiple AI model outputs against the original prescription image (the ultimate source of truth).
+
+Attached Inputs:
+1. Prescription image file
+2. Donut Model Output (layout-aware):
+   {json.dumps(donut_result, indent=2)}
+3. Phi-3 Model Output (medication refinement):
+   {json.dumps(phi3_results, indent=2)}
+
+Please follow these steps **in order**:
+
+1. **Extract & Normalize**
+   - Read the prescription image and extract: Patient Name, Date, Age, Physician Name, and each listed medication.
+   - Normalize dates to MM/DD/YYYY and medication names to their exact printed form.
+
+2. **Compare Model Outputs**
+   - For each field (Name, Date, Age, PhysicianName), check both model outputs and flag any discrepancies.
+   - For each medication entry, compare `drug_raw`, `dosage`, and `frequency` from Phi-3 with the layout cues from Donut.
+
+3. **Verify Against Image**
+   - Wherever the two models disagree, use the image text as the tiebreaker.
+   - If both models miss or misread something (e.g. a dosage or frequency), pull it directly from the image.
+
+4. **Error Correction**
+   - Correct spelling errors, unit inconsistencies (e.g. "mg" vs "MG"), and frequency shorthand (e.g. "BID" → "twice a day").
+
+5. **Assemble Final JSON**
+   - Populate exactly this schema; do not add extra keys.
+   - If a field is unreadable or absent on the image, set its value to `null`.
+
+**Final JSON Schema**
+```json
+{{
+  "Name": "string or null",
+  "Date": "string (MM/DD/YYYY) or null",
+  "Age": "string or null",
+  "PhysicianName": "string or null",
+  "Medications": [
+    {{
+      "drug_raw": "string",
+      "dosage": "string or null",
+      "frequency": "string or null",
+      "verification": {{
+        "standard_dosage": "string or null",
+        "side_effects": "string or null",
+        "interactions": "string or null"
+      }}
+    }}
+  ]
+}}
+"""
+
+    try:
+        response = gemini_client.generate_content(
+            [prompt, image],
+            generation_config={"response_mime_type": "application/json"},
+        )
+        result = json.loads(response.text)
+        
+        # Enhance with Google Search verification
+        for med in result.get("Medications", []):
+            if drug_name := med.get("drug_raw"):
+                verification = verify_medication_with_google(drug_name)
+                med["verification"] = {
+                    "standard_dosage": self._extract_dosage_info(verification),
+                    "side_effects": self._extract_side_effects(verification),
+                    "interactions": self._extract_interactions(verification)
+                }
+        
+        return result
+    except Exception as e:
+        logging.error(f"Gemini resolver failed: {e}")
+        return {"error": f"Gemini failed to resolve data: {e}"}
+
+def _extract_dosage_info(verification_data: Dict) -> Optional[str]:
+    """Extracts dosage information from verification results."""
+    for result in verification_data.get("verification_results", []):
+        if "standard dosage" in result.get("query", "").lower():
+            return result.get("results", [{}])[0].get("snippet")
+    return None
+
+def _extract_side_effects(verification_data: Dict) -> Optional[str]:
+    """Extracts side effects information from verification results."""
+    for result in verification_data.get("verification_results", []):
+        if "side effects" in result.get("query", "").lower():
+            return result.get("results", [{}])[0].get("snippet")
+    return None
+
+def _extract_interactions(verification_data: Dict) -> Optional[str]:
+    """Extracts drug interactions information from verification results."""
+    for result in verification_data.get("verification_results", []):
+        if "interactions" in result.get("query", "").lower():
+            return result.get("results", [{}])[0].get("snippet")
+    return None
+
+def extract_prescription_info(image_path: str) -> Dict[str, Any]:
+    """Runs the full hybrid AI pipeline."""
+    try:
+        image = Image.open(image_path).convert("RGB")
+
+        logging.info("Step 1: Running Donut model for layout analysis...")
+        donut_data = step1_run_donut(image)
+
+        medication_lines = [
+            item.get("text", "")
+            for item in donut_data.get("menu", [])
+            if "medi" in item.get("category", "").lower()
+        ]
+
+        logging.info("Step 2: Running Phi-3 model for medication refinement...")
+        phi3_refined_meds = [step2_run_phi3(line) for line in medication_lines]
+
+        logging.info("Step 3: Running Gemini model as the expert resolver...")
+        final_info = step3_run_gemini_resolver(image, donut_data, phi3_refined_meds)
+
+        if final_info.get("error"):
+            return final_info
+
+        result = {
+            "info": final_info,
+            "error": None,
+            "debug_info": {
+                "donut_output": donut_data,
+                "phi3_refinements": phi3_refined_meds,
+            },
+        }
+        return result
+
+    except Exception as e:
+        logging.error(f"Hybrid extraction pipeline failed: {e}", exc_info=True)
+        return {"error": f"An unexpected error occurred in the pipeline: {e}"}
diff --git a/vite.config.ts b/vite.config.ts
deleted file mode 100644
index d32eba1d63fd99b6a1751597fb072c9aca185dfd..0000000000000000000000000000000000000000
--- a/vite.config.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { defineConfig } from 'vite'
-import { svelte } from '@sveltejs/vite-plugin-svelte'
-
-// https://vite.dev/config/
-export default defineConfig({
-  plugins: [svelte()],
-})