app.py

import gradio as gr
from gradio_client import Client, handle_file
import os

# Define your Hugging Face token (make sure to set it as an environment variable)
HF_TOKEN = os.getenv("HF_TOKEN")  # Replace with your actual token if not using an environment variable

# Initialize the Gradio Client for the specified API
client = Client("on1onmangoes/CNIHUB101324v10", hf_token=HF_TOKEN)

# Update the conversation history within the function.
# Return the updated history along with any other required outputs.

client_name = ['rosariarossi','bianchifiordaliso','lorenzoverdi','lucia', 'quarto4', 'quinto5', 'secondo6', 'sesto6', 'settimo7','ottavo8','nono9']



def stream_chat_with_rag(
    message: str,
    history: list,
    client_name: str,
    system_prompt: str,
    num_retrieved_docs: int = 10,
    num_docs_final: int = 9,
    temperature: float = 0, 
    max_new_tokens: int = 1024, 
    top_p: float = 1.0, 
    top_k: int = 20, 
    penalty: float = 1.2,
):
    print(f"Message: {message}")
    print(f"History: {history}")

    # Build the conversation prompt including system prompt and history
    conversation = f"{system_prompt}\n\nFor Client: {client_name}\n"
    
    # Add previous conversation history
    for user_input, assistant_response in history:
        conversation += f"User: {user_input}\nAssistant: {assistant_response}\n"
    
    # Add the current user message
    conversation += f"User: {message}\nAssistant:"

    # Call the API with the user's message
    question = message
    answer = client.predict(question=question, api_name="/answer_with_rag")

    # Debugging: Print the raw response
    print("Raw answer from API:")
    print(answer)

    # Format the assistant's answer and the relevant documents separately
    formatted_answer = format_answer_string(answer)

    # Update the conversation history with the new message and answer
    history.append((message, formatted_answer))

    # Return the formatted answer
    return formatted_answer


def format_answer_string(answer: str):
    """
    This function extracts and formats the assistant's response before document metadata.
    Anything after the marker `[(` (where documents are listed) is ignored.
    """
    # Step 1: Split the response at the start of the document metadata
    split_marker = "[("
    if split_marker in answer:
        # Everything before the marker is the relevant answer
        answer_before_docs = answer.split(split_marker)[0]
    else:
        # If no documents metadata, return the entire answer
        answer_before_docs = answer
    
    # Step 2: Clean up formatting by replacing escaped newline characters
    formatted_answer = answer_before_docs.replace("\\n", "\n").strip()
    
    # Step 3: Remove potential starting and ending artifacts like (' and ,) if present
    if formatted_answer.startswith("(\"") and formatted_answer.endswith("\","):
        formatted_answer = formatted_answer[2:-2].strip()

    # Optional: Add a prefix for clarity
    formatted_answer = "Co-Pilot: " + formatted_answer
    
    return formatted_answer

def format_relevant_documents(relevant_docs: list):
    """
    This function formats the relevant document metadata and content for readable output.
    It extracts the heading, page number, and a snippet of the content from each document.
    """
    formatted_docs = "Relevant Documents:\n\n"
    
    for idx, (doc, score) in enumerate(relevant_docs):
        # Extract the relevant metadata
        heading = doc.metadata.get('heading', 'Unnamed Document')
        page_number = int(doc.metadata.get('page_number', -1))
        source = doc.metadata.get('source', 'Unknown Source')
        confidence = round(score, 4)  # Rounding the score for cleaner output
        
        # Add the formatted details to the output string
        formatted_docs += f"Document {idx + 1}:\n"
        formatted_docs += f"  - Heading: {heading}\n"
        formatted_docs += f"  - Page Number: {page_number}\n"
        formatted_docs += f"  - Source: {source}\n"
        formatted_docs += f"  - Confidence Score: {confidence}\n"
        
        # Optionally include a snippet from the content
        content_snippet = doc.page_content[:200]  # Take the first 200 characters for preview
        formatted_docs += f"  - Content Snippet: {content_snippet}...\n\n"

    return formatted_docs.strip()


# Function to handle PDF processing API call
def process_pdf(pdf_file, client_name):
    return client.predict(
        pdf_file=handle_file(pdf_file),
        client_name=client_name,  # Hardcoded client name
        api_name="/process_pdf2"
    )[1]  # Return only the result string

# Function to handle search API call
def search_api(query):
    return client.predict(query=query, api_name="/search_with_confidence")

# Function to handle RAG API call
def rag_api(question):
    return client.predict(question=question, api_name="/answer_with_rag")

# CSS for custom styling
CSS = """
# chat-container {
    height: 100vh;
}
"""

# Title for the application
TITLE = "<h1 style='text-align:center;'>CNI RAG QA v0</h1>"

# Create the Gradio Blocks interface
with gr.Blocks(css=CSS) as demo:
    gr.HTML(TITLE)
    with gr.Tab("Chat"):
        chatbot = gr.Chatbot()  # Create a chatbot interface

        chat_interface = gr.ChatInterface(
            fn=stream_chat_with_rag,
            chatbot=chatbot,
            additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
            additional_inputs=[
                gr.Dropdown(client_name,value="rosariarossi",label="Select Client", render=False,allow_custom_value=True),
                gr.Textbox(
                    value="You are an expert assistant",
                    label="System Prompt",
                    render=False,
                ),
                gr.Slider(
                    minimum=1,
                    maximum=10,
                    step=1,
                    value=10,
                    label="Number of Initial Documents to Retrieve",
                    render=False,
                ),
                gr.Slider(
                    minimum=1,
                    maximum=10,
                    step=1,
                    value=9,
                    label="Number of Final Documents to Retrieve",
                    render=False,
                ),
                gr.Slider(
                    minimum=0.2,
                    maximum=1,
                    step=0.1,
                    value=0,
                    label="Temperature",
                    render=False,
                ),
                gr.Slider(
                    minimum=128,
                    maximum=8192,
                    step=1,
                    value=1024,
                    label="Max new tokens",
                    render=False,
                ),
                gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    step=0.1,
                    value=1.0,
                    label="Top P",
                    render=False,
                ),
                gr.Slider(
                    minimum=1,
                    maximum=20,
                    step=1,
                    value=20,
                    label="Top K",
                    render=False,
                ),
                gr.Slider(
                    minimum=0.0,
                    maximum=2.0,
                    step=0.1,
                    value=1.2,
                    label="Repetition Penalty",
                    render=False,
                ),
            ],
        )

    # with gr.Tab("Process PDF"):
    #     pdf_input = gr.File(label="Upload PDF File")
    #     pdf_output = gr.Textbox(label="PDF Result", interactive=False)

    #     pdf_button = gr.Button("Process PDF")
    #     pdf_button.click(
    #         process_pdf,
    #         inputs=[pdf_input],
    #         outputs=pdf_output
    #     )

    with gr.Tab("Process PDF"):
        pdf_input = gr.File(label="Upload PDF File")
        select_client_dropdown = gr.Dropdown(client_name, value="rosariarossi", label="Select or Type Client", allow_custom_value=True)
        pdf_output = gr.Textbox(label="PDF Result", interactive=False)
    
        pdf_button = gr.Button("Process PDF")
        pdf_button.click(
            process_pdf,
            inputs=[pdf_input, select_client_dropdown],  # Pass both PDF and client name as inputs
            outputs=pdf_output
        )

    with gr.Tab("Answer with RAG"):
        question_input = gr.Textbox(label="Enter Question for RAG")
        answer_with_rag_select_client_dropdown = gr.Dropdown(client_name, value="rosariarossi", label="Select or Type Client", allow_custom_value=True)
        rag_output = gr.Textbox(label="RAG Answer Result", interactive=False)

        rag_button = gr.Button("Get Answer")
        rag_button.click(
            rag_api,
            inputs=[question_input,answer_with_rag_select_client_dropdown ],
            outputs=rag_output
        )

# Launch the app
if __name__ == "__main__":
    demo.launch()