app.py

import gradio as gr
from concurrent.futures import ThreadPoolExecutor
import fine_tuning
import rag

# -----------------------------
# Load fine-tuned model
# -----------------------------
fine_tuning.load_and_train()

# -----------------------------
# RAG Backend (Stub Example)
# -----------------------------
def generate_answer_rag(prompt, max_tokens=200):
    """
    Replace this stub with your actual RAG pipeline.
    For now, just a dummy response.
    """
    return f"[RAG answer for]: {prompt[:50]}..."


# -----------------------------
# Combined Answer Function
# -----------------------------
import time
from concurrent.futures import ThreadPoolExecutor, as_completed

def combined_generate(prompt, max_tokens):
    with ThreadPoolExecutor() as executor:
        start_times = {"Fine-tuned": time.time(), "RAG": time.time()}

        futures = {
            executor.submit(fine_tuning.generate_answer, model, tokenizer, device, prompt, max_tokens): "Fine-tuned",
            executor.submit(rag.generate_answer, prompt): "RAG",
        }

        answers = {"Fine-tuned": "", "RAG": ""}
        times = {"Fine-tuned": None, "RAG": None}

        for future in as_completed(futures):
            model_name = futures[future]
            answers[model_name] = future.result()
            times[model_name] = round(time.time() - start_times[model_name], 2)

            # Format answers with time taken
            ft_display = answers["Fine-tuned"]
            if times["Fine-tuned"] is not None:
                ft_display += f"\n\n⏱ Took {times['Fine-tuned']}s"

            rag_display = answers["RAG"]
            if times["RAG"] is not None:
                rag_display += f"\n\n⏱ Took {times['RAG']}s"

            yield ft_display, rag_display



# -----------------------------
# Gradio Interface
# -----------------------------
iface = gr.Interface(
    fn=combined_generate,
    inputs=[
        gr.Textbox(label="Enter your question:", lines=5, placeholder="Type your question here..."),
        gr.Slider(minimum=50, maximum=500, step=10, value=200, label="Max tokens to generate")
    ],
    outputs=[
        gr.Textbox(label="Fine-tuned Model Answer"),
        gr.Textbox(label="RAG Answer")
    ],
    title="Compare Fine-tuned Model vs RAG 🤖📚",
    description="Ask a question and get answers from both the fine-tuned model and the RAG pipeline."
).queue()

iface.launch()