Update app.py

app.py

@@ -1,171 +1,3 @@
-# import spaces
-# import gradio as gr
-# import os
-# import re
-# from pathlib import Path
-# from unidecode import unidecode
-
-# from langchain_community.document_loaders import PyPDFLoader
-# from langchain.text_splitter import RecursiveCharacterTextSplitter
-# from langchain_community.vectorstores import Chroma
-# from langchain.chains import ConversationalRetrievalChain
-# from langchain.memory import ConversationBufferMemory
-
-# from langchain_huggingface import HuggingFaceEmbeddings, HuggingFacePipeline
-# from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-
-# import chromadb
-# import torch
-# from concurrent.futures import ThreadPoolExecutor
-
-# # Environment configuration
-# os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-# # Predefined values
-# predefined_pdf = "t6.pdf"
-# predefined_llm = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # Use a smaller model for faster responses
-
-# def load_doc(list_file_path, chunk_size, chunk_overlap):
-#     loaders = [PyPDFLoader(x) for x in list_file_path]
-#     pages = []
-#     for loader in loaders:
-#         pages.extend(loader.load())
-#     text_splitter = RecursiveCharacterTextSplitter(
-#         chunk_size=chunk_size,
-#         chunk_overlap=chunk_overlap)
-#     doc_splits = text_splitter.split_documents(pages)
-#     return doc_splits
-
-# def create_db(splits, collection_name):
-#     embedding = HuggingFaceEmbeddings()
-#     new_client = chromadb.EphemeralClient()
-#     vectordb = Chroma.from_documents(
-#         documents=splits,
-#         embedding=embedding,
-#         client=new_client,
-#         collection_name=collection_name,
-#     )
-#     return vectordb
-
-# def load_db():
-#     embedding = HuggingFaceEmbeddings()
-#     vectordb = Chroma(
-#         embedding_function=embedding)
-#     return vectordb
-
-# def create_collection_name(filepath):
-#     collection_name = Path(filepath).stem
-#     collection_name = collection_name.replace(" ", "-")
-#     collection_name = unidecode(collection_name)
-#     collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-#     collection_name = collection_name[:50]
-#     if len(collection_name) < 3:
-#         collection_name = collection_name + 'xyz'
-#     if not collection_name[0].isalnum():
-#         collection_name = 'A' + collection_name[1:]
-#     if not collection_name[-1].isalnum():
-#         collection_name = collection_name[:-1] + 'Z'
-#     print('Filepath: ', filepath)
-#     print('Collection name: ', collection_name)
-#     return collection_name
-
-# def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
-#     if not torch.cuda.is_available():
-#         print("CUDA is not available. This demo does not work on CPU.")
-#         return None
-
-#     def init_llm():
-#         print("Initializing HF model and tokenizer...")
-#         model = AutoModelForCausalLM.from_pretrained(llm_model, device_map="auto", load_in_4bit=True)
-#         tokenizer = AutoTokenizer.from_pretrained(llm_model)
-#         tokenizer.use_default_system_prompt = False
-
-#         print("Initializing HF pipeline...")
-#         hf_pipeline = pipeline(
-#             "text-generation",
-#             model=model,
-#             tokenizer=tokenizer,
-#             device_map='auto',
-#             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=hf_pipeline, model_kwargs={'temperature': temperature})
-
-#         print("Defining buffer memory...")
-#         memory = ConversationBufferMemory(
-#             memory_key="chat_history",
-#             output_key='answer',
-#             return_messages=True
-#         )
-#         retriever = vector_db.as_retriever()
-        
-#         print("Defining retrieval chain...")
-#         qa_chain = ConversationalRetrievalChain.from_llm(
-#             llm,
-#             retriever=retriever,
-#             chain_type="stuff",
-#             memory=memory,
-#             return_source_documents=True,
-#             verbose=False,
-#         )
-#         return qa_chain
-
-#     with ThreadPoolExecutor() as executor:
-#         future = executor.submit(init_llm)
-#         qa_chain = future.result()
-        
-#     print("Initialization complete!")
-#     return qa_chain
-
-# # Define the conversation function
-# @spaces.GPU(duration=60)
-# def chat(message):
-#     global qa_chain
-#     prompt_template = "Instruction: You are an expert landlside assistant. Please provide a well written detailed and helpful answer to the following user query only from the given references.User Query:\n"
-#     full_input = prompt_template + message
-#     response = qa_chain({"question": full_input})
-#     full_answer = response["answer"]
-#     answer_parts = full_answer.split("Helpful Answer:")
-#     qa_chain.memory.clear()
-#     if len(answer_parts) > 1:
-#         main_answer = answer_parts[-1].strip()  # Extracting the main answer
-#         references = answer_parts[0].strip()  # Keeping the references
-#         answer = f"Helpful Answer: {main_answer}\n\nReferences:\n{references}"
-#     else:
-#         answer = full_answer  # In case there is no "Helpful Answer" part
-#     return answer, full_answer
-
-# interface = gr.Interface(
-#     fn=chat,
-#     inputs="textbox",  # Use a single input textbox
-#     outputs=["textbox", "textbox"],  # Two output fields: one for the main answer, one for other outputs
-#     title="LANDSLIDE AWARENESS CHATBOT",
-#     description="Ask me anything related to landlsides!",
-#     elem_id="my-interface",
-# )
-
-
-
-# # Load the PDF document and create the vector database (replace with your logic)
-# pdf_filepath = predefined_pdf
-# doc_splits = load_doc([pdf_filepath], chunk_size=400, chunk_overlap=40)
-# collection_name = create_collection_name(pdf_filepath)
-# vector_db = create_db(doc_splits, collection_name)
-
-# # Initialize the LLM chain with threading
-# qa_chain = initialize_llmchain(predefined_llm, temperature=0.7, max_tokens=512, top_k=5, vector_db=vector_db)
-
-# # Check if qa_chain is properly initialized
-# if qa_chain is None:
-#     print("Failed to initialize the QA chain. Please check the CUDA availability and model paths.")
-# else:
-#     # Launch the Gradio interface with share option
-#     interface.launch(share=True)
-
-
 import spaces
 import gradio as gr
 import os
@@ -292,7 +124,7 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
 @spaces.GPU(duration=60)
 def chat(message):
     global qa_chain
-    prompt_template = "Instruction: You are an expert landslide assistant. Please provide a well written detailed and helpful answer to the following user query only from the given references.User Query:\n"
+    prompt_template = "Instruction: You are an expert landlside assistant. Please provide a well written detailed and helpful answer to the following user query only from the given references.User Query:\n"
     full_input = prompt_template + message
     response = qa_chain({"question": full_input})
     full_answer = response["answer"]
@@ -309,12 +141,14 @@ def chat(message):
 interface = gr.Interface(
     fn=chat,
     inputs="textbox",  # Use a single input textbox
-    outputs=["chatbot", "markdown"],  # Use chatbot for chat interface and markdown for full response
+    outputs=["textbox", "textbox"],  # Two output fields: one for the main answer, one for other outputs
     title="LANDSLIDE AWARENESS CHATBOT",
-    description="Ask me anything related to landslides!",
+    description="Ask me anything related to landlsides!",
     elem_id="my-interface",
 )
 
+
+
 # Load the PDF document and create the vector database (replace with your logic)
 pdf_filepath = predefined_pdf
 doc_splits = load_doc([pdf_filepath], chunk_size=400, chunk_overlap=40)
@@ -330,3 +164,5 @@ if qa_chain is None:
 else:
     # Launch the Gradio interface with share option
     interface.launch(share=True)
+
+