Create app.py

app.py

@@ -0,0 +1,164 @@
+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 HuggingFaceHub
+from langchain.memory import ConversationBufferMemory
+import chromadb
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+# Constants and configuration
+list_llm = [
+    "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.2",
+    "mistralai/Mistral-7B-Instruct-v0.1", "HuggingFaceH4/zephyr-7b-beta",
+    "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]
+
+# Function placeholders (actual function implementations from the original script)
+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
+
+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
+
+def load_db():
+    embedding = HuggingFaceEmbeddings()
+    vectordb = Chroma(embedding_function=embedding)
+    return vectordb
+
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+    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}
+        )
+    else:
+        llm = HuggingFaceHub(
+            repo_id=llm_model, 
+            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
+        )
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+    retriever = vector_db.as_retriever()
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff", 
+        memory=memory,
+        return_source_documents=True,
+        return_generated_question=False,
+    )
+    return qa_chain
+
+def initialize_database(list_file_obj, chunk_size, chunk_overlap):
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    collection_name = os.path.basename(list_file_path[0]).replace(" ","-")[:50]
+    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!"
+
+def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db):
+    llm_name = list_llm[llm_option]
+    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
+    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)
+    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_source1_page = response_sources[0].metadata["page"] + 1
+    response_source2_page = response_sources[1].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
+
+def upload_file(file_obj):
+    list_file_path = [file.name for file in file_obj]
+    return list_file_path
+
+
+def gradio_ui():
+    with gr.Blocks(theme="base") as demo:
+        # States
+        vector_db, qa_chain, collection_name = gr.State(), gr.State(), gr.State()
+        db_progress, llm_progress = gr.Textbox(), gr.Textbox()
+        chatbot, doc_source1, source1_page, doc_source2, source2_page = gr.Chatbot(), gr.Textbox(), gr.Number(), gr.Textbox(), gr.Number()
+        msg = gr.Textbox(placeholder="Type message")
+
+        with gr.Tabs():
+            # Tab 1: Document Pre-processing
+            with gr.Tab("Step 1 - Document Pre-processing"):
+                with gr.Row():
+                    document = gr.File(label="Upload your PDF document", file_types=["pdf"])
+                with gr.Row():
+                    chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=50, label="Chunk size", interactive=True)
+                    chunk_overlap = gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Chunk overlap", interactive=True)
+                with gr.Row():
+                    db_init_btn = gr.Button("Initialize Vector Database")
+
+            # Tab 2: QA Chain Initialization
+            with gr.Tab("Step 2 - QA Chain Initialization"):
+                with gr.Row():
+                    llm_selection = gr.Radio(list_llm_simple, label="Choose LLM Model", value=list_llm_simple[0])
+                with gr.Row():
+                    temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, label="Temperature", interactive=True)
+                    max_tokens = gr.Slider(minimum=64, maximum=1024, value=256, step=64, label="Max Tokens", interactive=True)
+                    top_k = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Top K", interactive=True)
+                with gr.Row():
+                    qa_init_btn = gr.Button("Initialize QA Chain")
+
+            # Tab 3: Conversation with Chatbot
+            with gr.Tab("Step 3 - Conversation with Chatbot"):
+                chat_history = gr.State()
+                with gr.Row():
+                    chatbot
+                with gr.Row():
+                    msg
+                    submit_btn = gr.Button("Submit")
+
+        # Handlers
+        db_init_btn.click(initialize_database, inputs=[document, chunk_size, chunk_overlap], outputs=[vector_db, collection_name, db_progress])
+        qa_init_btn.click(initialize_LLM, inputs=[llm_selection, temperature, max_tokens, top_k, vector_db], outputs=[qa_chain, llm_progress])
+        submit_btn.click(conversation, inputs=[qa_chain, msg, chat_history], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page])
+
+    return demo
+
+if __name__ == "__main__":
+    gradio_ui().launch()