Update app.py

app.py

@@ -17,12 +17,16 @@ try:
     from langchain_community.vectorstores import FAISS
     from langchain.prompts import PromptTemplate
     from langchain.chains import RetrievalQA
-    # This is the key change: Import HuggingFaceHub instead of HuggingFaceEndpoint
-    from langchain_community.llms import HuggingFaceHub
+    from langchain_huggingface import HuggingFaceEndpoint  # Updated import
     LANGCHAIN_AVAILABLE = True
 except ImportError as e:
-    logger.error(f"LangChain import error: {e}")
-    LANGCHAIN_AVAILABLE = False
+    try:
+        # Fallback to older import structure
+        from langchain_community.llms import HuggingFaceEndpoint
+        LANGCHAIN_AVAILABLE = True
+    except ImportError as e2:
+        logger.error(f"LangChain import error: {e}, {e2}")
+        LANGCHAIN_AVAILABLE = False
 
 # Create PDFs folder if it doesn't exist
 PDF_FOLDER_PATH = "./pdfs"
@@ -63,23 +67,38 @@ def create_llm():
     hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
     
     try:
-        # The crucial change: Use HuggingFaceHub directly as suggested
-        # Note: You need to specify a repo_id that is a text generation model.
-        # "mistralai/Mistral-7B-Instruct-v0.2" is a good choice for instruction following.
-        llm = HuggingFaceHub(
-            repo_id="mistralai/Mistral-7B-Instruct-v0.2", # Using the suggested model
-            huggingfacehub_api_token=hf_token,
-            model_kwargs={
-                "temperature": 0.7,
-                "max_length": 512, # Note: max_new_tokens is typically preferred for generation length
-                "do_sample": True,
-                "top_p": 0.9,
-                "top_k": 50
-            }
-        )
-        logger.info(f"Successfully initialized LLM with model: mistralai/Mistral-7B-Instruct-v0.2")
-        return llm
-            
+        # Try different model configurations
+        models_to_try = [
+            "microsoft/DialoGPT-medium",
+            "google/flan-t5-base", 
+            "microsoft/DialoGPT-small",
+            "tiiuae/falcon-7b-instruct"
+        ]
+        
+        for model_id in models_to_try:
+            try:
+                llm = HuggingFaceEndpoint(
+                    repo_id=model_id,
+                    temperature=0.7,
+                    max_new_tokens=512,
+                    huggingfacehub_api_token=hf_token,
+                    model_kwargs={
+                        "max_length": 512,
+                        "do_sample": True,
+                        "temperature": 0.7,
+                        "top_p": 0.9,
+                        "top_k": 50
+                    }
+                )
+                logger.info(f"Successfully initialized LLM with model: {model_id}")
+                return llm
+            except Exception as model_error:
+                logger.warning(f"Failed to initialize {model_id}: {model_error}")
+                continue
+        
+        # If all models fail, raise the last error
+        raise Exception("All model initialization attempts failed")
+        
     except Exception as e:
         logger.error(f"LLM creation error: {e}")
         # Return a simple fallback that doesn't use HuggingFace API
@@ -529,7 +548,7 @@ def create_interface():
         if PRELOADED_PDFS:
             gr.Markdown("""
             <div style="background: linear-gradient(90deg, #10b981, #059669); 
-                                color: white; padding: 12px; border-radius: 8px; margin: 10px 0;">
+                        color: white; padding: 12px; border-radius: 8px; margin: 10px 0;">
             🎉 <strong>Pre-loaded PDFs detected!</strong> Use the 'Load Pre-existing PDFs' button to get started quickly.
             </div>
             """)