Update app.py

app.py

@@ -1,388 +1,153 @@
+
+from typing import Any
 import gradio as gr
-import os
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
 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 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
-import torch
-import tqdm 
-import accelerate
-import re
-from huggingface_hub import InferenceClient
-from huggingface_hub import login, HfApi
-
-
-
-# Authenticate using the Hugging Face token stored as a secret
-def authenticate_hf():
-    token = os.getenv("HF")
-    if not token:
-        raise ValueError("Hugging Face token not found in environment variables.")
-    
-    try:
-        login(token=token)
-        api = HfApi()
-        user_info = api.whoami(token=token)
-        print(f"Login successful. User info: {user_info}")
-    except Exception as e:
-        raise ValueError(f"Error during login: {e}")
-
-# Authenticate at the start of the script
-authenticate_hf()
-
-
-
 
-# 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
+from langchain.chains import ConversationalRetrievalChain
+from langchain_openai import ChatOpenAI
 
+from langchain_community.document_loaders import PyMuPDFLoader
 
-# 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
-    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,
+import fitz
+from PIL import Image
+import os
+import re
+import openai
+
+openai.api_key = "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"
+
+
+def add_text(history, text: str):
+    if not text:
+        raise gr.Error("Enter text")
+    history = history + [(text, "")]
+    return history
+
+
+class MyApp:
+    def __init__(self) -> None:
+        self.OPENAI_API_KEY: str = openai.api_key
+        self.chain = None
+        self.chat_history: list = []
+        self.N: int = 0
+        self.count: int = 0
+
+    def __call__(self, file: str) -> Any:
+        if self.count == 0:
+            self.chain = self.build_chain(file)
+            self.count += 1
+        return self.chain
+
+    def process_file(self, file: str):
+        loader = PyMuPDFLoader(file.name)
+        documents = loader.load()
+        pattern = r"/([^/]+)$"
+        match = re.search(pattern, file.name)
+        try:
+            file_name = match.group(1)
+        except:
+            file_name = os.path.basename(file)
+
+        return documents, file_name
+
+    def build_chain(self, file: str):
+        documents, file_name = self.process_file(file)
+        # Load embeddings model
+        embeddings = OpenAIEmbeddings(openai_api_key=self.OPENAI_API_KEY)
+        pdfsearch = Chroma.from_documents(
+            documents,
+            embeddings,
+            collection_name=file_name,
         )
-    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,
+        chain = ConversationalRetrievalChain.from_llm(
+            ChatOpenAI(temperature=0.0, openai_api_key=self.OPENAI_API_KEY),
+            retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
+            return_source_documents=True,
         )
-    
-    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,
-    )
-    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)
-    return qa_chain, "Complete!"
-
+        return chain
 
-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:
-        response_answer = response_answer.split("Helpful Answer:")[-1]
-    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] 
-    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()
-        
-        
-        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():
-                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)
+def get_response(history, query, file):
+    if not file:
+        raise gr.Error(message="Upload a PDF")
+    chain = app(file)
+    result = chain(
+        {"question": query, "chat_history": app.chat_history}, return_only_outputs=True
+    )
+    app.chat_history += [(query, result["answer"])]
+    app.N = list(result["source_documents"][0])[1][1]["page"]
+    for char in result["answer"]:
+        history[-1][-1] += char
+        yield history, ""
+
+
+def render_file(file):
+    doc = fitz.open(file.name)
+    page = doc[app.N]
+    # Render the page as a PNG image with a resolution of 150 DPI
+    pix = page.get_pixmap(dpi=150)
+    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
+    return image
+
+
+def purge_chat_and_render_first(file):
+    print("purge_chat_and_render_first")
+    # Purges the previous chat session so that the bot has no concept of previous documents
+    app.chat_history = []
+    app.count = 0
+
+    # Use PyMuPDF to render the first page of the uploaded document
+    doc = fitz.open(file.name)
+    page = doc[0]
+    # Render the page as a PNG image with a resolution of 150 DPI
+    pix = page.get_pixmap(dpi=150)
+    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
+    return image, []
+
+
+app = MyApp()
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        with gr.Row():
+            with gr.Column(scale=2):
                 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 3 - Initialize QA chain"):
-            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):
+                    chatbot = gr.Chatbot(value=[], elem_id="chatbot")
                 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)
+                    txt = gr.Textbox(
+                        show_label=False,
+                        placeholder="Enter text and press submit",
+                        scale=2
+                    )
+                    submit_btn = gr.Button("Submit", scale=1)
+
+            with gr.Column(scale=1):
                 with gr.Row():
-                    slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                    show_img = gr.Image(label="Upload PDF")
                 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")
+                    btn = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"])
 
-        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.Row():
-                msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
-            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)
+    btn.upload(
+        fn=purge_chat_and_render_first,
+        inputs=[btn],
+        outputs=[show_img, chatbot],
+    )
+
+    submit_btn.click(
+        fn=add_text,
+        inputs=[chatbot, txt],
+        outputs=[
+            chatbot,
+        ],
+        queue=False,
+    ).success(
+        fn=get_response, inputs=[chatbot, txt, btn], outputs=[chatbot, txt]
+    ).success(
+        fn=render_file, inputs=[btn], outputs=[show_img]
+    )
 
-        # 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)
+demo.queue()
+demo.launch()
 
 
-if __name__ == "__main__":
-    demo()