Update app.py

app.py

@@ -84,87 +84,13 @@ def load_db():
 # Initialize langchain LLM chain
 def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
     progress(0.1, desc="Initializing HF tokenizer...")
-    # HuggingFacePipeline uses local model
-    # Note: it will download model locally...
-    # tokenizer=AutoTokenizer.from_pretrained(llm_model)
-    # progress(0.5, desc="Initializing HF pipeline...")
-    # pipeline=transformers.pipeline(
-    #     "text-generation",
-    #     model=llm_model,
-    #     tokenizer=tokenizer,
-    #     torch_dtype=torch.bfloat16,
-    #     trust_remote_code=True,
-    #     device_map="auto",
-    #     # max_length=1024,
-    #     max_new_tokens=max_tokens,
-    #     do_sample=True,
-    #     top_k=top_k,
-    #     num_return_sequences=1,
-    #     eos_token_id=tokenizer.eos_token_id
-    #     )
-    # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
+
     
     # HuggingFaceHub uses HF inference endpoints
     progress(0.5, desc="Initializing HF Hub...")
     vertexai.init(project="imgcp-ff81e7053b072ce5", location="us-central1")
     llm = VertexAI(model_name="gemini-pro")
-    # Use of trust_remote_code as model_kwargs
-    # Warning: langchain issue
-    # URL: https://github.com/langchain-ai/langchain/issues/6080
-    # if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
-    #     llm = HuggingFaceEndpoint(
-    #         repo_id=llm_model, 
-    #         # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
-    #         temperature = temperature,
-    #         max_new_tokens = max_tokens,
-    #         top_k = top_k,
-    #         load_in_8bit = True,
-    #     )
-    # elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]:
-    #     raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint")
-    #     llm = HuggingFaceEndpoint(
-    #         repo_id=llm_model, 
-    #         temperature = temperature,
-    #         max_new_tokens = max_tokens,
-    #         top_k = top_k,
-    #     )
-    # elif llm_model == "microsoft/phi-2":
-    #     # raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
-    #     llm = HuggingFaceEndpoint(
-    #         repo_id=llm_model, 
-    #         # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
-    #         temperature = temperature,
-    #         max_new_tokens = max_tokens,
-    #         top_k = top_k,
-    #         trust_remote_code = True,
-    #         torch_dtype = "auto",
-    #     )
-    # elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
-    #     llm = HuggingFaceEndpoint(
-    #         repo_id=llm_model, 
-    #         # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
-    #         temperature = temperature,
-    #         max_new_tokens = 250,
-    #         top_k = top_k,
-    #     )
-    # elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
-    #     raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
-    #     llm = HuggingFaceEndpoint(
-    #         repo_id=llm_model, 
-    #         # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
-    #         temperature = temperature,
-    #         max_new_tokens = max_tokens,
-    #         top_k = top_k,
-    #     )
-    # else:
-    #     llm = HuggingFaceEndpoint(
-    #         repo_id=llm_model, 
-    #         # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
-    #         # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
-    #         temperature = temperature,
-    #         max_new_tokens = max_tokens,
-    #         top_k = top_k,
-    #     )
+
     
     progress(0.75, desc="Defining buffer memory...")
     memory = ConversationBufferMemory(