Update app.py

app.py

@@ -1,34 +1,40 @@
 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_community.embeddings import HuggingFaceEmbeddings
+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
 
-def summarize_document(document_text):
-    # Your summarization code here
-    summary = "The document covers various topics such as X, Y, and Z, providing detailed insights into each aspect."
-    return summary
-
-def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    collection_name = create_collection_name(list_file_path[0])
-    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
-    vector_db = create_db(doc_splits, collection_name)
-    return vector_db, collection_name, "Complete!"
+list_llm = ["HuggingFaceH4/zephyr-7b-beta", "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):
     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)
+        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()
@@ -40,22 +46,217 @@ def create_db(splits, collection_name):
     )
     return vectordb
 
-def create_collection_name(filepath):
-    collection_name = Path(filepath).stem
-    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'
-    return collection_name
+# 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 Hub...")
+    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model, 
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+            load_in_8bit=True,
+        )
+    # Add other LLM models initialization here
+    progress(0.75, desc="Defining buffer memory...")
+    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", 
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+    progress(0.9, desc="Done!")
+    return qa_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)
+    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()
+    response_source3 = response_sources[2].page_content.strip()
+    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
+    new_history = history + [(message, response_answer)]
+    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)
+    return list_file_path
 
 def demo():
-    with gr.Interface(summarize_document, inputs="text", outputs="text", title="PDF Summarizer") as iface:
-        iface.launch()
+    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</center></h2>
+        <h3>Ask any questions about your PDF documents</h3>""")
+        
+        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)")
+        
+        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)
+                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):
+                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)
+                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(label="LLM initialization", value="None")
+            with gr.Row():
+                llm_btn = gr.Button("Initialize LLM chain")
+            
+        with gr.Tab("Step 4 - Chat"):
+            with gr.Row():
+                message = gr.Textbox(label="Your question")
+                ask_btn = gr.Button("Ask")
+            with gr.Row():
+                answer = gr.Textbox(label="Answer", value="Ask your question to get an answer")
+                chat_history = gr.Textbox(label="Chat history", value="Chat history")
+                source1 = gr.Textbox(label="Source 1", value="Source 1")
+                source2 = gr.Textbox(label="Source 2", value="Source 2")
+                source3 = gr.Textbox(label="Source 3", value="Source 3")
+
+    @demo.func
+    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)
+        return list_file_path
+    
+    @demo.func
+    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
+    
+    @demo.func
+    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
+
+    @demo.func
+    def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+        progress(0.1, desc="Initializing HF Hub...")
+        if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
+            llm = HuggingFaceEndpoint(
+                repo_id=llm_model, 
+                temperature=temperature,
+                max_new_tokens=max_tokens,
+                top_k=top_k,
+                load_in_8bit=True,
+            )
+        # Add other LLM models initialization here
+        progress(0.75, desc="Defining buffer memory...")
+        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", 
+            memory=memory,
+            return_source_documents=True,
+            verbose=False,
+        )
+        progress(0.9, desc="Done!")
+        return qa_chain
+
+    @demo.func
+    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
+
+    @demo.func
+    def conversation(qa_chain, message, history):
+        formatted_chat_history = format_chat_history(message, history)
+        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()
+        response_source3 = response_sources[2].page_content.strip()
+        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
+        new_history = history + [(message, response_answer)]
+        return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
+
+    # Define file upload actions
+    demo.upload_file(upload_file)
+
+    # Define document processing actions
+    demo.load_doc(load_doc)
+    demo.create_db(create_db)
+
+    # Define LLM chain initialization actions
+    demo.initialize_llmchain(initialize_llmchain)
+
+    # Define conversation actions
+    demo.format_chat_history(format_chat_history)
+    demo.conversation(conversation)
 
-if __name__ == "__main__":
-    demo()
+    demo.launch()