Update app.py

app.py

@@ -1,58 +1,24 @@
 import gradio as gr
 import os
-
 from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Chroma
 from langchain.chains import ConversationalRetrievalChain
 from langchain.embeddings import HuggingFaceEmbeddings 
-from langchain.llms import HuggingFacePipeline
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
 from langchain.llms import HuggingFaceHub
-
 from pathlib import Path
 import chromadb
 
-from transformers import AutoTokenizer
-import transformers
-import torch
-import tqdm 
-import accelerate
-
-
-# default_persist_directory = './chroma_HF/'
-
-llm_name0 = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-llm_name1 = "mistralai/Mistral-7B-Instruct-v0.2"
-llm_name2 = "mistralai/Mistral-7B-Instruct-v0.1"
-llm_name3 = "meta-llama/Llama-2-7b-chat-hf"
-llm_name4 = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-llm_name5 = "microsoft/phi-2"
-llm_name6 = "mosaicml/mpt-7b-instruct"
-llm_name7 = "tiiuae/falcon-7b-instruct"
-llm_name8 = "google/flan-t5-xxl"
-list_llm = [llm_name0, llm_name1, llm_name2, llm_name3, llm_name4, llm_name5, llm_name6, llm_name7, llm_name8]
-list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+llm_names = ["mistralai/Mixtral-8x7B-Instruct-v0.1"]
+llm_names_simple = [os.path.basename(llm) for llm in llm_names]
 
-# 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)
+    pages = [loader.load() for loader in loaders]
+    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()
@@ -61,254 +27,113 @@ 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
+    model_kwargs = {"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
     if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
-        llm = HuggingFaceHub(
-            repo_id=llm_model, 
-            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
-        )
-    elif llm_model == "microsoft/phi-2":
-        raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
-        llm = HuggingFaceHub(
-            repo_id=llm_model, 
-            model_kwargs={"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 = HuggingFaceHub(
-            repo_id=llm_model, 
-            model_kwargs={"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 = HuggingFaceHub(
-            repo_id=llm_model, 
-            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
-        )
-    else:
-        llm = HuggingFaceHub(
-            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}
-        )
-    
+        model_kwargs["load_in_8bit"] = True
+    llm = HuggingFaceHub(repo_id=llm_model, model_kwargs=model_kwargs)
     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()
+    memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True)
+    retriever = vector_db.as_retriever()
     progress(0.8, desc="Defining retrieval chain...")
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         retriever=retriever,
-        chain_type="stuff", 
+        chain_type="stuff",
         memory=memory,
-        # combine_docs_chain_kwargs={"prompt": your_prompt})
         return_source_documents=True,
-        # return_generated_question=True,
-        # verbose=True,
     )
     progress(0.9, desc="Done!")
     return qa_chain
 
-
-# Initialize database
 def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
-    # Create list of documents (when valid)
-    #file_path = file_obj.name
     list_file_path = [x.name for x in list_file_obj if x is not None]
     collection_name = Path(list_file_path[0]).stem
-    # print('list_file_path: ', list_file_path)
-    # print('Collection name: ', collection_name)
     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)
+    llm_name = llm_names[llm_option]
     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}")
+    formatted_chat_history = [f"User: {user_message}\nAssistant: {bot_message}" for user_message, bot_message in chat_history]
     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"]
     response_sources = response["source_documents"]
     response_source1 = response_sources[0].page_content.strip()
     response_source2 = response_sources[1].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
-    # 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
-    
 
 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)
+    list_file_path = [file_obj.name for _ in file_obj]
     return list_file_path
 
-
 def demo():
     with gr.Blocks(theme="base") as demo:
         vector_db = gr.State()
         qa_chain = gr.State()
         collection_name = gr.State()
-        
-        gr.Markdown(
-        """<center><h2>PDF-based chatbot (powered by LangChain and open-source LLMs)</center></h2>
-        <h3>Ask any questions about your PDF documents, along with follow-ups</h3>
-        <b>Note:</b> This AI assistant performs retrieval-augmented generation from your PDF documents. \
-        When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.</i>
-        <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate an output.<br>
-        """)
-        with gr.Tab("Step 1 - Document pre-processing"):
-            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.Row():
-                db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
+
+        gr.Markdown("""<center><h2>ChatPDF</center></h2>""")
+
+        with gr.Tab("Step 1 - Selezione PDF"):
+            document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
+            db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value="ChromaDB", type="index", info="Choose your vector database")
             with gr.Accordion("Advanced options - Document text splitter", open=False):
-                with gr.Row():
-                    slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
-                with gr.Row():
-                    slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
-            with gr.Row():
-                db_progress = gr.Textbox(label="Vector database initialization", value="None")
-            with gr.Row():
-                db_btn = gr.Button("Generate vector database...")
-            
-        with gr.Tab("Step 2 - QA chain initialization"):
-            with gr.Row():
-                llm_btn = gr.Radio(list_llm_simple, \
-                    label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
-            with gr.Accordion("Advanced options - LLM model", open=False):
-                with gr.Row():
-                    slider_temperature = gr.Slider(minimum = 0.0, 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)
-                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)
-            with gr.Row():
-                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
-            with gr.Row():
-                qachain_btn = gr.Button("Initialize question-answering chain...")
+                slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
+                slider_chunk_overlap = gr.Slider(minimum=10, maximum=200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
+            db_progress = gr.Textbox(label="Vector database initialization", value="None")
+            db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress])
 
-        with gr.Tab("Step 3 - Conversation with chatbot"):
+        with gr.Tab("Step 2 - Inizializzazione QA"):
+            llm_btn = gr.Radio(llm_names_simple, label="LLM models", value=llm_names_simple[0], type="index", info="Choose your LLM model")
+            with gr.Accordion("Advanced options - LLM model", open=False):
+                slider_temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
+                slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
+            llm_progress = gr.Textbox(value="None", label="QA chain initialization")
+            qachain_btn = gr.Button("Initialize question-answering chain...")
+            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], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], queue=False)
+
+        with gr.Tab("Step 3 - Conversazione con 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():
-                msg = gr.Textbox(placeholder="Type message", container=True)
-            with gr.Row():
-                submit_btn = gr.Button("Submit")
-                clear_btn = gr.ClearButton([msg, chatbot])
-            
-        # 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], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], \
-            queue=False)
+            with gr.Accordion("Advanced - Document references", open=True):
+                doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
+                source1_page = gr.Number(label="Page", scale=1)
+                doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
+                source2_page = gr.Number(label="Page", scale=1)
+            msg = gr.Textbox(placeholder="Type message", container=True)
+            submit_btn = gr.Button("Submit")
+            clear_btn = gr.ClearButton([msg, chatbot])
+
+        msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_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], queue=False)
+        clear_btn.click(lambda: [None, "", 0, "", 0], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], queue=False)
 
-        # Chatbot events
-        msg.submit(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_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], \
-            queue=False)
-        clear_btn.click(lambda:[None,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], \
-            queue=False)
     demo.queue().launch(debug=True)
 
-
 if __name__ == "__main__":
-    demo()
+    demo()