Update app.py

app.py

@@ -1,27 +1,36 @@
 import gradio as gr
+import os
+api_token = os.getenv("HF_TOKEN")
+
+
 from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
 from langchain.chains import ConversationalRetrievalChain
-from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.embeddings import HuggingFaceEmbeddings 
+from langchain_community.llms import HuggingFacePipeline
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
-import os
-
-api_token = os.getenv("HF_TOKEN")
+from langchain_community.llms import HuggingFaceEndpoint
+import torch
 
-# List of LLMs
-list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
+list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]  
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
-# Load and split PDF documents
+# Load and split PDF document
 def load_doc(list_file_path):
+    # 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=1024, chunk_overlap=64)
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = 1024, 
+        chunk_overlap = 64 
+    )  
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
@@ -31,23 +40,24 @@ def create_db(splits):
     vectordb = FAISS.from_documents(splits, embeddings)
     return vectordb
 
-# Initialize LLM chain
-def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+
+# Initialize langchain LLM chain
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
     if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
         llm = HuggingFaceEndpoint(
             repo_id=llm_model,
-            huggingfacehub_api_token=api_token,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            top_k=top_k,
+            huggingfacehub_api_token = api_token,
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
         )
     else:
         llm = HuggingFaceEndpoint(
-            huggingfacehub_api_token=api_token,
-            repo_id=llm_model,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            top_k=top_k,
+            huggingfacehub_api_token = api_token,
+            repo_id=llm_model, 
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
         )
     
     memory = ConversationBufferMemory(
@@ -56,98 +66,151 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
         return_messages=True
     )
 
-    retriever = vector_db.as_retriever()
+    retriever=vector_db.as_retriever()
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         retriever=retriever,
-        chain_type="stuff",
+        chain_type="stuff", 
         memory=memory,
         return_source_documents=True,
         verbose=False,
     )
     return qa_chain
 
-# Function to handle chatbot responses
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    vector_db,
-    llm_model,
-):
-    # Initialize LLM chain if not already initialized
-    if not hasattr(respond, 'qa_chain'):
-        respond.qa_chain = initialize_llmchain(llm_model, temperature, max_tokens, top_p, vector_db)
-
-    # Format chat history
+# Initialize database
+def initialize_database(list_file_obj, progress=gr.Progress()):
+    # Create a list of documents (when valid)
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    # Load document and create splits
+    doc_splits = load_doc(list_file_path)
+    # Create or load vector database
+    vector_db = create_db(doc_splits)
+    return vector_db, "Database created!"
+
+# Initialize LLM
+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, "QA chain initialized. Chatbot is ready!"
+
+
+def format_chat_history(message, chat_history):
     formatted_chat_history = []
-    for user_message, bot_message in 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.append(f"User: {message}")
+    return formatted_chat_history
+    
 
+def conversation(qa_chain, message, history):
+    formatted_chat_history = format_chat_history(message, history)
     # Generate response using QA chain
-    response = respond.qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
+    response = qa_chain.invoke({"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
+    # Append user message and response to chat history
+    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.Blocks(theme=gr.themes.Default(primary_hue="sky")) as demo:
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue = "sky")) as demo:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        gr.HTML("<center><h1>RAG PDF chatbot</h1><center>")
+        gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \
+        <b>Please do not upload confidential documents.</b>
+        """)
+        with gr.Row():
+            with gr.Column(scale = 86):
+                gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
+                with gr.Row():
+                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
+                with gr.Row():
+                    db_btn = gr.Button("Create vector database")
+                with gr.Row():
+                        db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", 
+                gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
+                with gr.Row():
+                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False
+                with gr.Row():
+                    with gr.Accordion("LLM input parameters", open=False):
+                        with gr.Row():
+                            slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True)
+                        with gr.Row():
+                            slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True)
+                        with gr.Row():
+                                slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True)
+                with gr.Row():
+                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
+                with gr.Row():
+                        llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", 
+
+            with gr.Column(scale = 200):
+                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
+                chatbot = gr.Chatbot(height=505)
+                with gr.Accordion("Relevent context from the source document", 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="Ask a question", container=True)
+                with gr.Row():
+                    submit_btn = gr.Button("Submit")
+                    clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
+            
+        # Preprocessing events
+        db_btn.click(initialize_database, \
+            inputs=[document], \
+            outputs=[vector_db, 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)
+
+        # 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)
 
-    return response_answer
-
-# CSS for styling the interface
-css = """
-body {
-    background-color: #06688E; /* Dark background */
-    color: white; /* Text color for better visibility */
-}
-.gr-button {
-    background-color: #42B3CE !important; /* White button color */
-    color: black !important; /* Black text for contrast */
-    border: none !important;
-    padding: 8px 16px !important;
-    border-radius: 5px !important;
-}
-.gr-button:hover {
-    background-color: #e0e0e0 !important; /* Slightly lighter button on hover */
-}
-.gr-slider-container {
-    color: white !important; /* Slider labels in white */
-}
-"""
-
-# Initialize database and LLM chain
-def initialize_database_and_llm(list_file_obj, llm_option, max_tokens, temperature, top_p):
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    doc_splits = load_doc(list_file_path)
-    vector_db = create_db(doc_splits)
-    llm_name = list_llm[llm_option]
-    return vector_db, llm_name
-
-# Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Files(file_count="multiple", file_types=["pdf"], label="Upload PDF documents", visible=False),
-        gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple, visible=False),
-        gr.Slider(minimum=128, maximum=9192, value=4096, step=128, label="Max new tokens", visible=False),
-        gr.Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.1, label="Temperature", visible=False),
-        gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Top-k", visible=False),
-    ],
-    css=css,
-    title="RAG PDF Chatbot",
-    description="Query your PDF documents using a Retrieval Augmented Generation (RAG) chatbot.",
-)
-
-# Preprocessing events
-demo.preprocess(
-    initialize_database_and_llm,
-    inputs=["document", "llm_btn", "slider_maxtokens", "slider_temperature", "slider_topk"],
-    outputs=["vector_db", "llm_model"],
-    api_name="initialize",
-)
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo()