src/interface.py

import csv
import gradio as gr

# Define a function to export conversations to CSV
def export_conversations(conversations):
    # Create a CSV writer
    csvfile = "conversations.csv"
    with open(csvfile, "w", newline="") as f:
        writer = csv.writer(f)
        
        # Write the header row
        writer.writerow(["User Input", "Model Response"])
        
        # Iterate over the conversations and write each row
        for conversation in conversations:
            for i, (user_input, model_response) in enumerate(conversation):
                writer.writerow([user_input, model_response])
    
    return csvfile

# Gradio application setup
def create_demo():
    with gr.Blocks(title="LLAMA 3 Rag Chat pdf", theme="Monochrome") as demo:
        # App Description
        gr.Markdown(
            """
            ## LLAMA 3 Rag chat pdf

            This application allows you to experiment with LLAMA 3 8B Instruct model for RAG.             
            You can adjust various parameters to control the model's output. 

            Original https://huggingface.co/spaces/ModularityAI/LLama3Rag

            experimented on making RAG for long pdf documents
            """
        )

        with gr.Row():
            # Chatbot and Image Column (75% space)
            with gr.Column(scale=0.95):
                with gr.Row():
                    chat_history = gr.Chatbot(value=[], elem_id='chatbot', height=480)
                    show_img = gr.Image(label='Uploaded PDF', height=480)

            # Sliders Column (25% space)
            with gr.Column(scale=0.05):
                with gr.Row():
                    slider_chunk_size = gr.Slider(
                        minimum=256, maximum=32768, value=1024, label="Chunk Size", elem_id='slider1'
                    )
                with gr.Row():
                    slider_overlap_percentage = gr.Slider(
                        minimum=0, maximum=99, value=50, label="Chunk Overlap Percentage", elem_id='slider2'
                    )
                with gr.Row():
                    slider_temp = gr.Slider(
                        minimum=0, maximum=1, value=0.5, label="Model Temperature", elem_id='slider3'
                    )
                with gr.Row():
                    slider_k = gr.Slider(
                        minimum=1, step=1,maximum=20, value=2, label="Max Chunks in Context", elem_id='slider2'
                    )

        # Input and Submit Button Row
        with gr.Row():
            with gr.Column(scale=0.60):
                text_input = gr.Textbox(
                    show_label=False,
                    placeholder="Type here to ask your PDF",
                    container=False
                )
            with gr.Column(scale=0.20):
                submit_button = gr.Button('Send')
            with gr.Column(scale=0.20):
                uploaded_pdf = gr.UploadButton("📁 Upload PDF", file_types=[".pdf"], elem_id='upload_pdf')

    return demo, chat_history, show_img, text_input, submit_button, uploaded_pdf, slider_chunk_size,slider_overlap_percentage,slider_temp,slider_k

if __name__ == '__main__':
    demo, chatbot, show_img, text_input, submit_button, uploaded_pdf, slider_chunk_size,slider_overlap_percentage,slider_temp,slider_k = create_demo()
    demo.queue()
    demo.launch()