Update model_tools.py

model_tools.py

@@ -1,66 +1,57 @@
-# model_tools.py
-
-import ollama
-import requests
-from bs4 import BeautifulSoup
-
-# ---- LLM Task Extractor ----
-
-def extract_task(user_input: str) -> str:
-    """
-    Use local Ollama LLM to classify user query into Hugging Face task.
-    """
-    prompt = f"""
-You are an AI agent helping a developer select the right ML model.
-Given this request: "{user_input}"
-
-Reply with only the corresponding Hugging Face task like:
-- text-classification
-- summarization
-- translation
-- image-classification
-- etc.
-Only reply with the task name, and nothing else.
-    """
-
-    response = ollama.chat(
-        model="mistral",  # Replace with llama3, phi3, etc. if needed
-        messages=[{"role": "user", "content": prompt}]
-    )
-
-    return response['message']['content'].strip().lower()
-
-# ---- Hugging Face Scraper ----
-
-def scrape_huggingface_models(task: str, max_results=5) -> list[dict]:
-    """
-    Scrapes Hugging Face for top models for a given task.
-    """
-    url = f"https://huggingface.co/models?pipeline_tag={task}&sort=downloads"
-
-    try:
-        resp = requests.get(url)
-        soup = BeautifulSoup(resp.text, "html.parser")
-        model_cards = soup.find_all("article", class_="model-card")[:max_results]
-
-        results = []
-        for card in model_cards:
-            name_tag = card.find("a", class_="model-link")
-            model_name = name_tag.text.strip() if name_tag else "unknown"
-
-            task_div = card.find("div", class_="task-tag")
-            task_name = task_div.text.strip() if task_div else task
-
-            arch = "encoder-decoder" if "bart" in model_name.lower() or "t5" in model_name.lower() else "unknown"
-
-            results.append({
-                "model_name": model_name,
-                "task": task_name,
-                "architecture": arch
-            })
-
-        return results
-
-    except Exception as e:
-        print(f"Scraping error: {e}")
-        return []
+# model_tools.py
+
+import ollama
+import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline
+
+# ---- LLM Task Extractor ----
+
+# Load T5-based model once
+task_extractor = pipeline("text2text-generation", model="google/flan-t5-small")
+
+def extract_task(user_input):
+    prompt = f"Classify the following ML task: {user_input}. Just reply with the task name."
+    result = task_extractor(prompt, max_new_tokens=10)
+    return result[0]["generated_text"].strip().lower()
+    response = ollama.chat(
+        model="mistral",  # Replace with llama3, phi3, etc. if needed
+        messages=[{"role": "user", "content": prompt}]
+    )
+
+    return response['message']['content'].strip().lower()
+
+# ---- Hugging Face Scraper ----
+
+def scrape_huggingface_models(task: str, max_results=5) -> list[dict]:
+    """
+    Scrapes Hugging Face for top models for a given task.
+    """
+    url = f"https://huggingface.co/models?pipeline_tag={task}&sort=downloads"
+
+    try:
+        resp = requests.get(url)
+        soup = BeautifulSoup(resp.text, "html.parser")
+        model_cards = soup.find_all("article", class_="model-card")[:max_results]
+
+        results = []
+        for card in model_cards:
+            name_tag = card.find("a", class_="model-link")
+            model_name = name_tag.text.strip() if name_tag else "unknown"
+
+            task_div = card.find("div", class_="task-tag")
+            task_name = task_div.text.strip() if task_div else task
+
+            arch = "encoder-decoder" if "bart" in model_name.lower() or "t5" in model_name.lower() else "unknown"
+
+            results.append({
+                "model_name": model_name,
+                "task": task_name,
+                "architecture": arch
+            })
+
+        return results
+
+    except Exception as e:
+        print(f"Scraping error: {e}")
+        return []