Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import os
 from dotenv import load_dotenv
-
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import Chroma
@@ -11,13 +10,11 @@ from langchain_community.llms import HuggingFacePipeline
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
 from langchain_huggingface.llms import HuggingFaceEndpoint
-#from huggingface_hub import login  # Import the login function
-
+from huggingface_hub import login
 from pathlib import Path
 import chromadb
 from unidecode import unidecode
-
-# from transformers import AutoTokenizer
+from transformers import AutoTokenizer
 import transformers
 import torch
 import tqdm 
@@ -28,30 +25,20 @@ load_dotenv()
 
 huggingface_api_key = os.getenv("HUGGINGFACE_API_KEY")
 
-# print('HF TOKEN: ', huggingface_api_key)
+print('HF TOKEN: ', huggingface_api_key)
 
-# default_persist_directory = './chroma_HF/'
-list_llm = ["mistralai/Mistral-7B-Instruct-v0.3"]
+list_llm = ["mistralai/Mistral-7B-Instruct-v0.2"]
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
-# Load PDF document and create doc splits
 def load_doc(list_file_path, chunk_size, chunk_overlap):
-    # Processing for one document only
-    # loader = PyPDFLoader(file_path)
-    # pages = loader.load()
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
     for loader in loaders:
         pages.extend(loader.load())
     text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
-    #text_splitter = RecursiveCharacterTextSplitter(
-    #    chunk_size = chunk_size, 
-    #    chunk_overlap = chunk_overlap)
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
-
-# Create vector database
 def create_db(splits, collection_name):
     embedding = HuggingFaceEmbeddings()
     new_client = chromadb.EphemeralClient()
@@ -60,100 +47,50 @@ def create_db(splits, collection_name):
         embedding=embedding,
         client=new_client,
         collection_name=collection_name,
-        # persist_directory=default_persist_directory
     )
     return vectordb
 
-
-# Load vector database
 def load_db():
     embedding = HuggingFaceEmbeddings()
-    vectordb = Chroma(
-        # persist_directory=default_persist_directory, 
-        embedding_function=embedding)
+    vectordb = Chroma(embedding_function=embedding)
     return vectordb
 
-
-# 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...")
-    # Use of trust_remote_code as model_kwargs
-    # Warning: langchain issue
-    # URL: https://github.com/langchain-ai/langchain/issues/6080
-    #login(token=huggingface_api_key)
     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(
         memory_key="chat_history",
         output_key='answer',
         return_messages=True
     )
-    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
-    retriever=vector_db.as_retriever()
+    retriever = vector_db.as_retriever()
     progress(0.8, desc="Defining retrieval chain...")
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         retriever=retriever,
         chain_type="stuff", 
         memory=memory,
-        # combine_docs_chain_kwargs={"prompt": your_prompt})
         return_source_documents=True,
-        #return_generated_question=False,
         verbose=False,
     )
     progress(0.9, desc="Done!")
     return qa_chain
 
-
-# Generate collection name for vector database
-#  - Use filepath as input, ensuring unicode text
 def create_collection_name(filepath):
-    # Extract filename without extension
     collection_name = Path(filepath).stem
-    # Fix potential issues from naming convention
-    ## Remove space
     collection_name = collection_name.replace(" ","-") 
-    ## ASCII transliterations of Unicode text
     collection_name = unidecode(collection_name)
-    ## Remove special characters
-    #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
     collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-    ## Limit length to 50 characters
     collection_name = collection_name[:50]
-    ## Minimum length of 3 characters
     if len(collection_name) < 3:
         collection_name = collection_name + 'xyz'
-    ## Enforce start and end as alphanumeric character
     if not collection_name[0].isalnum():
         collection_name = 'A' + collection_name[1:]
     if not collection_name[-1].isalnum():
@@ -162,46 +99,32 @@ def create_collection_name(filepath):
     print('Collection name: ', collection_name)
     return collection_name
 
-
-# Initialize database
 def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
-    # Create list of documents (when valid)
     list_file_path = [x.name for x in list_file_obj if x is not None]
-    # Create collection_name for vector database
     progress(0.1, desc="Creating collection name...")
     collection_name = create_collection_name(list_file_path[0])
     progress(0.25, desc="Loading document...")
-    # Load document and create splits
     doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
-    # Create or load vector database
     progress(0.5, desc="Generating vector database...")
-    # global vector_db
     vector_db = create_db(doc_splits, collection_name)
     progress(0.9, desc="Done!")
     return vector_db, collection_name, "Complete!"
 
-
 def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    # print("llm_option",llm_option)
     llm_name = list_llm[llm_option]
     print("llm_name: ",llm_name)
     qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
     return qa_chain, "Complete!"
 
-
 def format_chat_history(message, chat_history):
     formatted_chat_history = []
     for user_message, bot_message in chat_history:
         formatted_chat_history.append(f"User: {user_message}")
         formatted_chat_history.append(f"Assistant: {bot_message}")
     return formatted_chat_history
-    
 
 def conversation(qa_chain, message, history):
     formatted_chat_history = format_chat_history(message, history)
-    #print("formatted_chat_history",formatted_chat_history)
-   
-    # Generate response using QA chain
     response = qa_chain({"question": message, "chat_history": formatted_chat_history})
     response_answer = response["answer"]
     if response_answer.find("Helpful Answer:") != -1:
@@ -210,29 +133,19 @@ def conversation(qa_chain, message, history):
     response_source1 = response_sources[0].page_content.strip()
     response_source2 = response_sources[1].page_content.strip()
     response_source3 = response_sources[2].page_content.strip()
-    # Langchain sources are zero-based
     response_source1_page = response_sources[0].metadata["page"] + 1
     response_source2_page = response_sources[1].metadata["page"] + 1
     response_source3_page = response_sources[2].metadata["page"] + 1
-    # print ('chat response: ', response_answer)
-    # print('DB source', response_sources)
-    
-    # Append user message and response to chat history
     new_history = history + [(message, response_answer)]
-    # return gr.update(value=""), new_history, response_sources[0], response_sources[1] 
     return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
-    
 
 def upload_file(file_obj):
     list_file_path = []
     for idx, file in enumerate(file_obj):
         file_path = file_obj.name
         list_file_path.append(file_path)
-    # print(file_path)
-    # initialize_database(file_path, progress)
     return list_file_path
 
-
 def demo():
     with gr.Blocks(theme="base") as demo:
         vector_db = gr.State()
@@ -252,7 +165,6 @@ def demo():
         with gr.Tab("Step 1 - Upload PDF"):
             with gr.Row():
                 document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
-                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
         
         with gr.Tab("Step 2 - Process document"):
             with gr.Row():
@@ -265,7 +177,7 @@ def demo():
             with gr.Row():
                 db_progress = gr.Textbox(label="Vector database initialization", value="None")
             with gr.Row():
-                db_btn = gr.Button("Generate vector database")
+                db_generate_btn = gr.Button("Generate vector database")
             
         with gr.Tab("Step 3 - Initialize QA chain"):
             with gr.Row():
@@ -275,59 +187,38 @@ def demo():
                 with gr.Row():
                     slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
                 with gr.Row():
-                    slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                    slider_maxtokens = gr.Slider(minimum = 32, maximum = 2048, value=1024, step=16, label="Max tokens", info="Maximum tokens", interactive=True)
                 with gr.Row():
-                    slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
+                    slider_topk = gr.Slider(minimum = 10, maximum = 50, value=40, step=2, label="Top K", info="Top K", interactive=True)
             with gr.Row():
-                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
+                llm_progress = gr.Textbox(label="LLM initialization", value="None")
             with gr.Row():
-                qachain_btn = gr.Button("Initialize Question Answering chain")
+                llm_generate_btn = gr.Button("Initialize LLM chain")
 
-        with gr.Tab("Step 4 - Chatbot"):
-            chatbot = gr.Chatbot(height=300)
-            with gr.Accordion("Advanced - Document references", open=False):
-                with gr.Row():
-                    doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
-                    source1_page = gr.Number(label="Page", scale=1)
-                with gr.Row():
-                    doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
-                    source2_page = gr.Number(label="Page", scale=1)
-                with gr.Row():
-                    doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
-                    source3_page = gr.Number(label="Page", scale=1)
+        with gr.Tab("Step 4 - Ask questions to your chatbot"):
             with gr.Row():
-                msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
+                chatbot = gr.Chatbot(label="Langchain PDF chatbot", height=400)
             with gr.Row():
-                submit_btn = gr.Button("Submit message")
-                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
-            
-        # Preprocessing events
-        #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
-        db_btn.click(initialize_database, \
-            inputs=[document, slider_chunk_size, slider_chunk_overlap], \
-            outputs=[vector_db, collection_name, db_progress])
-        qachain_btn.click(initialize_LLM, \
-            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
-            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
+                msg = gr.Textbox(label="Ask anything about your PDF document", placeholder="Type your message here...", show_label=False).style(container=False)
+            with gr.Row():
+                response_source1 = gr.Textbox(label="Source document #1", value="", interactive=False)
+                response_source1_page = gr.Number(label="Page", value=0, interactive=False)
+            with gr.Row():
+                response_source2 = gr.Textbox(label="Source document #2", value="", interactive=False)
+                response_source2_page = gr.Number(label="Page", value=0, interactive=False)
+            with gr.Row():
+                response_source3 = gr.Textbox(label="Source document #3", value="", interactive=False)
+                response_source3_page = gr.Number(label="Page", value=0, interactive=False)
+            with gr.Row():
+                clear = gr.Button("Clear")
 
-        # Chatbot events
-        msg.submit(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-        submit_btn.click(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-    demo.queue().launch(debug=True)
+        document.upload(upload_file, [document], [document])
+        db_generate_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress])
+        llm_generate_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], outputs=[qa_chain, llm_progress])
+        msg.submit(conversation, [qa_chain, msg, chatbot], [qa_chain, msg, chatbot, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page])
+        clear.click(lambda: None, None, chatbot, queue=False)
 
+    return demo
 
 if __name__ == "__main__":
- demo().queue().launch(debug=True, server_port=7861)
+    demo().queue().launch(debug=True, server_port=7861)  # Use a different port, e.g., 7861