Update app.py

app.py

@@ -1,216 +1,95 @@
 import gradio as gr
+from huggingface_hub import InferenceClient
 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.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
-import torch
-
-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 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 
-    )  
-    doc_splits = text_splitter.split_documents(pages)
-    return doc_splits
-
-# Create vector database
-def create_db(splits):
-    embeddings = HuggingFaceEmbeddings()
-    vectordb = FAISS.from_documents(splits, embeddings)
-    return vectordb
-
-
-# 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,
-        )
-    else:
-        llm = HuggingFaceEndpoint(
-            huggingfacehub_api_token = api_token,
-            repo_id=llm_model, 
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
+# Mock vector database creation
+vector_db_created = False
+
+def create_vector_db(uploaded_files):
+    global vector_db_created
+    if uploaded_files:
+        vector_db_created = True
+        return "Vector database created successfully. You can now chat with your documents!"
+    return "Please upload a file first."
+
+# Initialize Chat Model
+client = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    if not vector_db_created:
+        yield "Error: Please create the vector database first."
+        return
     
-    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,
-        verbose=False,
-    )
-    return qa_chain
-
-# 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 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)
-    # Generate response using QA chain
-    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>
+    messages = [{"role": "system", "content": system_message}]
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+    messages.append({"role": "user", "content": message})
+    response = ""
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+        response += token
+        yield response
+
+# Custom CSS
+css = """
+#drop-area { border: 2px dashed #42B3CE; border-radius: 10px; padding: 20px; }
+.error-message { color: red; font-weight: bold; }
+.vector-btn { background-color: #42B3CE !important; color: white; }
+.chat-submit { background-color: #06688E !important; color: white; }
+.chat-clear { background-color: #e0e0e0 !important; color: black; }
+"""
+
+def main():
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown("""# **RAG PDF Chatbot**
+        Query your PDF documents! Upload, initialize, and chat using an AI assistant.
         """)
-        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)
 
+        # Step 1: File upload and database initialization
+        with gr.Row():
+            with gr.Column():
+                pdf_upload = gr.File(label="Upload PDF documents", file_types=[".pdf"], type="file")
+                create_db_btn = gr.Button("Create vector database", elem_classes=["vector-btn"])
+                db_status = gr.Textbox("Not initialized", interactive=False)
+
+            with gr.Column():
+                gr.Markdown("**Step 2 - Chat with your Document**")
+                chatbot = gr.ChatInterface(
+                    respond,
+                    additional_inputs=[
+                        gr.Textbox(
+                            value="You are a helpful assistant...",
+                            label="System Message",
+                            visible=False
+                        ),
+                        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens", visible=False),
+                        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature", visible=False),
+                        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p", visible=False),
+                    ],
+                    submit_btn="Submit",
+                    clear_btn="Clear",
+                )
+
+        # Button events
+        create_db_btn.click(create_vector_db, inputs=[pdf_upload], outputs=[db_status])
+
+    demo.launch(share=True)
 
 if __name__ == "__main__":
-    demo()
+    main()