Upload app.py

app.py

@@ -13,7 +13,7 @@ QWEN_API_URL = "Qwen/Qwen2.5-Max-Demo"  # Gradio API for Qwen2.5 chat
 CHUNK_SIZE = 800
 TOP_K_RESULTS = 150
 SIMILARITY_THRESHOLD = 0.4
-PASSWORD_HASH = os.getenv("PASSWORD_HASH", "default_password")  # Use environment variable for password
+PASSWORD_HASH = os.getenv("PASSWORD_HASH", "abc12345")  # Use environment variable for password
 
 BASE_SYSTEM_PROMPT = """
 Répondez en français selon ces règles :
@@ -117,12 +117,18 @@ def create_new_database(file_content: str, db_name: str, password: str, progress
             text_embeddings=list(zip(chunks, embeddings_list)),
             embedding=embeddings
         )
-        vector_store.save_local(".")
-        logging.info(f"FAISS database saved to: {faiss_file} and {pkl_file}")
+
+        # Save FAISS database
+        try:
+            vector_store.save_local(".")
+            logging.info(f"FAISS database saved to: {faiss_file} and {pkl_file}")
+        except Exception as e:
+            logging.error(f"FAISS save error: {str(e)}")
+            return "Failed to save FAISS database. Please check logs for details.", []
 
         # Verify files were created
         if not os.path.exists(faiss_file) or not os.path.exists(pkl_file):
-            return f"Failed to save FAISS database files.", []
+            return "Failed to save FAISS database files. Please check file permissions.", []
         logging.info(f"FAISS database files created: {faiss_file}, {pkl_file}")
 
         # Update the list of available databases
@@ -145,7 +151,11 @@ def generate_response(user_input: str, db_name: str) -> str:
         if not os.path.exists(faiss_file) or not os.path.exists(pkl_file):
             return f"Database '{db_name}' does not exist."
 
-        vector_store = FAISS.load_local(".", embeddings, allow_dangerous_deserialization=True)
+        try:
+            vector_store = FAISS.load_local(".", embeddings, allow_dangerous_deserialization=True)
+        except Exception as e:
+            logging.error(f"FAISS load error: {str(e)}")
+            return "Failed to load FAISS database. Please check logs for details."
 
         # Contextual search
         docs_scores = vector_store.similarity_search_with_score(