Update app.py

app.py

@@ -140,23 +140,18 @@ def evaluate_rag_pipeline(qa_chain, dataset):
     return avg_results
 
 # Initialize langchain LLM chain
-def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model,
-            huggingfacehub_api_token=api_token,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            top_k=top_k,
-        )
-    else:
-        llm = HuggingFaceEndpoint(
-            huggingfacehub_api_token=api_token,
-            repo_id=llm_model,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            top_k=top_k,
-        )
+def initialize_llmchain(llm_choice, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    # Get the full model name from the index
+    llm_model = list_llm[llm_choice]
+    
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        huggingfacehub_api_token=api_token,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k,
+        model=llm_model  # Add model parameter
+    )
     
     memory = ConversationBufferMemory(
         memory_key="chat_history",
@@ -173,7 +168,7 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
         return_source_documents=True,
         verbose=False,
     )
-    return qa_chain
+    return qa_chain, "LLM initialized successfully!"
 
 def initialize_database(list_file_obj, splitting_strategy, db_choice, progress=gr.Progress()):
     list_file_path = [x.name for x in list_file_obj if x is not None]