Update app.py

app.py

@@ -1,67 +1,67 @@
-from langchain.chains import RetrievalQA
-from langchain_community.llms import HuggingFacePipeline
-from transformers import pipeline
-from modules import parser, vectorizer
-from datetime import datetime
-import re
+import gradio as gr
+from modules import analysis  # Make sure this matches your folder structure
 
-def filter_logs_by_time(logs_text, start_time, end_time):
-    """
-    Filters log lines based on timestamp range.
-    """
-    if not start_time or not end_time:
-        return logs_text  # Skip filtering if not both are set
+# Create the Gradio interface using Blocks
+with gr.Blocks(title="Log Intelligence Assistant") as demo:
+    gr.Markdown("## 🔍 Interactive Network Log Assistant")
 
-    start = datetime.fromisoformat(str(start_time))
-    end = datetime.fromisoformat(str(end_time))
+    # ---------- Inputs ----------
+    with gr.Tab("Inputs 🔽"):
+        with gr.Row():
+            uploaded_files = gr.File(
+                file_types=[".pdf", ".log", ".txt"],
+                file_count="multiple",
+                label="📁 Upload Log Files"
+            )
+            text_input = gr.Textbox(
+                label="Paste Logs Here",
+                lines=15,
+                placeholder="2023-10-05 14:22:11 [Firewall] BLOCKED: 192.168.1.5..."
+            )
 
-    filtered_lines = []
-    for line in logs_text.splitlines():
-        match = re.match(r"(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})", line)
-        if match:
-            timestamp = datetime.strptime(match.group(1), "%Y-%m-%d %H:%M:%S")
-            if start <= timestamp <= end:
-                filtered_lines.append(line)
-    return "\n".join(filtered_lines)
+        with gr.Row():
+            query_input = gr.Textbox(
+                label="Ask About Logs",
+                placeholder="Find failed login attempts from 192.168.1.5"
+            )
+            quick_query = gr.Dropdown(
+                choices=["", "Detect anomalies", "Show blocked IPs", "Generate summary"],
+                label="Quick Actions"
+            )
 
-def run_analysis(uploaded_files, text_input, query, quick_action, temperature, start_time, end_time):
-    logs_text = ""
+        with gr.Accordion("⚙️ Configuration Controls", open=False):
+            temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Creativity (Temperature)")
+            start_time = gr.DateTime(label="Start Time")
+            end_time = gr.DateTime(label="End Time")
 
-    # Combine uploaded + pasted logs
-    if uploaded_files:
-        logs_text += parser.parse_uploaded_files(uploaded_files)
-    if text_input:
-        logs_text += "\n" + text_input
+    # ---------- Outputs ----------
+    with gr.Tab("Outputs 🔼"):
+        analysis_result = gr.Textbox(label="Analysis Results", lines=10, interactive=False)
 
-    if not logs_text.strip():
-        return "❌ No logs provided.", None, None, None
+        # Use gr.Plot for matplotlib/plotly compatibility
+        bar_plot = gr.Plot(label="Event Frequency by Hour")
+        pie_chart = gr.Plot(label="Event Type Distribution")
 
-    # Filter logs based on time range (if provided)
-    logs_text = filter_logs_by_time(logs_text, start_time, end_time)
+        alert_feed = gr.HighlightedText(label="Security Alerts", color_map={
+            "CRITICAL": "red",
+            "WARNING": "orange",
+            "INFO": "blue"
+        })
 
-    # Use either typed query or quick action
-    query_text = query.strip() if query else ""
-    if not query_text and quick_action:
-        query_text = quick_action
-    if not query_text:
-        return "❌ No query or quick action selected.", None, None, None
+    # ---------- Export ----------
+    with gr.Tab("Export 📤"):
+        export_csv = gr.Button("Save as CSV")
+        export_pdf = gr.Button("Export PDF Report")
 
-    # Process logs
-    docs = vectorizer.prepare_documents(logs_text)
-    vectordb = vectorizer.create_vectorstore(docs)
+    # ---------- Action Button ----------
+    run_button = gr.Button("🔎 Analyze Logs")
 
-    pipe = pipeline("text-generation", model="gpt2", max_length=512, temperature=temperature)
-    llm = HuggingFacePipeline(pipeline=pipe)
-
-    qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectordb.as_retriever())
-    result = qa.run(query_text)
-
-    # Dummy charts and alerts for testing
-    bar_data = {"Hour": ["14:00", "15:00"], "Count": [8, 4]}
-    pie_data = {"Event Type": ["Blocked", "Scan"], "Count": [8, 4]}
-    alerts = [("CRITICAL", "8 blocked SSH attempts from 192.168.1.5"),
-              ("WARNING", "4 port scanning alerts from 10.0.0.8")]
-
-    return result, bar_data, pie_data, alerts
+    # Bind the Analyze button to your backend function
+    run_button.click(
+        fn=analysis.run_analysis,
+        inputs=[uploaded_files, text_input, query_input, quick_query, temperature, start_time, end_time],
+        outputs=[analysis_result, bar_plot, pie_chart, alert_feed]
+    )
 
+# ✅ Make sure this line exists — it launches the app!
 demo.launch()