Update app.py

app.py

@@ -1,358 +1,193 @@
 import gradio as gr
 import os
-
-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_community.embeddings import HuggingFaceEmbeddings 
-from langchain_community.llms import HuggingFacePipeline
-from langchain.chains import ConversationChain
+from googletrans import Translator
+import requests
+from dotenv import load_dotenv
+import numpy as np
+from langchain.embeddings import Chroma
+from langchain.vectorstores import Chroma
+from langchain.document_loaders import UnstructuredPDFLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.qa_with_sources import load_qa_with_sources_from_chain_type
+from langchain.schema import Document
 from langchain.memory import ConversationBufferMemory
-from langchain_community.llms import HuggingFaceEndpoint
-
-from pathlib import Path
-import chromadb
-from unidecode import unidecode
-
-from transformers import AutoTokenizer
-import transformers
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.llms.base import LLM
+from typing import List, Dict, Any, Optional
+from pydantic import BaseModel
+from tqdm import tqdm
 import torch
-import tqdm 
-import accelerate
-import re
-
-
-
-# default_persist_directory = './chroma_HF/'
-list_llm = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1", \
-    "google/gemma-7b-it","google/gemma-2b-it", \
-    "HuggingFaceH4/zephyr-7b-beta", "HuggingFaceH4/zephyr-7b-gemma-v0.1", \
-    "meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2", \
-    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct", \
-    "google/flan-t5-xxl"
-]
-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()
-    vectordb = Chroma.from_documents(
-        documents=splits,
-        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)
-    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})
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class PDFDocument(Document):
+    def _extract_metadata(self, **kwargs) -> Dict[str, Any]:
+        metadata = super()._extract_metadata(**kwargs)
+        metadata["filename"] = self.page_content
+        return metadata
+
+def initialize_database(document, chunk_size, chunk_overlap, progress=gr.Progress()):
+    logger.info("Initializing database...")
+    embedding_function = Chroma.from_pretrained("chroma-rt")
+    documents = []
+    for file in document:
+        loader = UnstructuredPDFLoader(file.name)
+        docs = loader.load()
+        splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+        for doc in docs:
+            pages = splitter.split_document(doc)
+            for page in pages:
+                documents.append(PDFDocument(page_content=page.page_content, metadata={"filename": file.name}))
+    vectorstore = Chroma.create_index(embedding_function, documents)
+    progress.update(0.5)
+    logger.info("Database initialized successfully.")
+    return vectorstore, "Initialized"
+
+def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress(), language="italian"):
+    logger.info("Initializing LLM chain...")
+    llm_name = list_llm[llm_option]
+    print("llm_name: ",llm_name)
     
-    # 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
-    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,
-        )
+    if language == "italian":
+        default_llm = "google/gemma-7b-it"
     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,
-        )
+        default_llm = "mistralai/Mistral-7B-Instruct-v0.2"
     
-    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()
-    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,
+    if llm_name != default_llm:
+        print(f"Using default LLM {default_llm} for {language}")
+        llm_name = default_llm
+    
+    qa_chain = load_qa_with_sources_from_chain_type(
+        llm=llm_name,
+        chain_type="stuff",
+        retriever=vector_db.as_retriever(),
+        temperature=llm_temperature,
+        top_k_per_token=top_k,
+        max_tokens=max_tokens,
     )
-    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():
-        collection_name = collection_name[:-1] + 'Z'
-    print('Filepath: ', 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)
+    progress.update(1.0)
+    logger.info("LLM chain initialized successfully.")
     return qa_chain, "Complete!"
 
+def format_chat_history(message, history):
+    chat_history = ""
+    for item in history:
+        chat_history += f"\nUser: {item[0]}\nAI: {item[1]}"
+    chat_history += f"\n\nUser: {message}"
+    return chat_history
 
-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 translate_text(text, src_lang, dest_lang):
+    translator = Translator()
+    result = translator.translate(text, src=src_lang, dest=dest_lang)
+    return result.text
 
-def conversation(qa_chain, message, history):
+def conversation(qa_chain, message, history, language):
     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:
+    if response_answer.find("Helpful Answer:")!= -1:
         response_answer = response_answer.split("Helpful Answer:")[-1]
+    
+    if language != "italian":
+        try:
+            translated_response = translate_text(response_answer, src="en", dest="it")
+        except Exception as e:
+            logger.error(f"Error translating response: {e}")
+            translated_response = response_answer
+    else:
+        translated_response = response_answer
+    
     response_sources = response["source_documents"]
     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] 
+    new_history = history + [(message, translated_response)]
     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()
         qa_chain = gr.State()
         collection_name = gr.State()
+        language = gr.State(default_value="italian")
         
         gr.Markdown(
-        """<center><h2>PDF-based chatbot</center></h2>
-        <h3>Ask any questions about your PDF documents</h3>""")
+        """<center><h2>Chatbot basato su PDF</center></h2>
+        <h3>Fai domande sui tuoi documenti PDF</h3>""")
         gr.Markdown(
-        """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
-        The user interface explicitely shows multiple steps to help understand the RAG workflow. 
-        This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
-        <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 a reply.
+        """<b>Note:</b> Questo assistente AI, utilizzando Langchain e LLM open-source, esegue retrieval-augmented generation (RAG) dai tuoi documenti PDF. \
+        L'interfaccia utente mostra esplicitamente più passaggi per aiutare a comprendere il flusso di lavoro RAG. 
+        Questo chatbot tiene conto delle domande precedenti quando genera risposte (tramite memoria conversazionale), e include riferimenti al documento per scopi di chiarezza.<br>
+        <br><b>Avviso:</b> Questo spazio utilizza l'hardware CPU Basic gratuito da Hugging Face. Alcuni passaggi e modelli LLM utilizzati qui sotto (endpoint di inferenza gratuiti) possono richiedere del tempo per generare una risposta.
         """)
         
-        with gr.Tab("Step 1 - Upload PDF"):
+        with gr.Tab("Step 1 - Carica 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)
+                document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Carica i tuoi documenti PDF (singolo o multiplo)")
         
-        with gr.Tab("Step 2 - Process document"):
+        with gr.Tab("Step 2 - Processa documento"):
             with gr.Row():
-                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):
+                db_btn = gr.Radio(["ChromaDB"], label="Tipo di database vettoriale", value = "ChromaDB", type="index", info="Scegli il tuo database vettoriale")
+            with gr.Accordion("Opzioni avanzate - Divisore testo documento", 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)
+                    slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Dimensione chunk", info="Dimensione chunk", 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)
+                    slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label=" Sovrapposizione chunk", info="Sovrapposizione chunk", interactive=True)
             with gr.Row():
-                db_progress = gr.Textbox(label="Vector database initialization", value="None")
+                db_progress = gr.Textbox(label="Inizializzazione database vettoriale", value="Nessuno")
             with gr.Row():
-                db_btn = gr.Button("Generate vector database")
+                db_btn = gr.Button("Genera database vettoriale")
             
-        with gr.Tab("Step 3 - Initialize QA chain"):
+        with gr.Tab("Step 3 - Inizializza catena QA"):
             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):
+                    label="Modelli LLM", value = list_llm_simple[0], type="index", info="Scegli il tuo modello LLM")
+            with gr.Accordion("Opzioni avanzate - Modello LLM", open=False):
                 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)
+                    slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperatura", info="Temperatura del modello", 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 = 224, maximum = 4096, value=1024, step=32, label="Token massimi", info="Token massimi del modello", 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 = 1, maximum = 10, value=3, step=1, label="Campioni top-k", info="Campioni top-k del modello", interactive=True)
             with gr.Row():
-                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
+                llm_progress = gr.Textbox(value="Nessuno",label="Inizializzazione catena QA")
             with gr.Row():
-                qachain_btn = gr.Button("Initialize Question Answering chain")
+                qachain_btn = gr.Button("Inizializza catena Question Answering")
 
         with gr.Tab("Step 4 - Chatbot"):
             chatbot = gr.Chatbot(height=300)
-            with gr.Accordion("Advanced - Document references", open=False):
+            with gr.Accordion("Avanzate - Riferimenti documento", 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)
+                    doc_source1 = gr.Textbox(label="Riferimento 1", lines=2, container=True, scale=20)
+                    source1_page = gr.Number(label="Pagina", 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)
+                    doc_source2 = gr.Textbox(label="Riferimento 2", lines=2, container=True, scale=20)
+                    source2_page = gr.Number(label="Pagina", 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)
+                    doc_source3 = gr.Textbox(label="Riferimento 3", lines=2, container=True, scale=20)
+                    source3_page = gr.Number(label="Pagina", scale=1)
             with gr.Row():
-                msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
+                msg = gr.Textbox(placeholder="Digita un messaggio (es. 'Di cosa parla questo documento?')", container=True)
             with gr.Row():
-                submit_btn = gr.Button("Submit message")
-                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
+                submit_btn = gr.Button("Invia messaggio")
+                clear_btn = gr.ClearButton([msg, chatbot], value="Pulisci conversazione")
+            with gr.Row():
+                language_selector = gr.Radio(choices=["italiano", "inglese"], value="italiano", label="Lingua")
             
         # 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], \
+            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db, language], \
             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], \
@@ -360,15 +195,18 @@ def demo():
 
         # Chatbot events
         msg.submit(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
+            inputs=[qa_chain, msg, chatbot, language], \
             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], \
+            inputs=[qa_chain, msg, chatbot, language], \
             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```
+		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)