app.py

import gradio as gr
import os
import threading
import base64
from io import BytesIO
from groq import Groq

# 🔹 Initialize Groq API Client (FREE)
groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))

# 🔹 Load Text-to-Image Models (Restoring Multi-Image Generation)
model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA")
model2 = gr.load("models/Purz/face-projection")
model3 = gr.load("models/stablediffusion/stable-diffusion-xl")

# 🔹 Stop Event for Threading
stop_event = threading.Event()

# 🔹 Convert PIL image to Base64
def pil_to_base64(pil_image, image_format="jpeg"):
    buffered = BytesIO()
    pil_image.save(buffered, format=image_format)
    base64_string = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return base64_string, image_format

# 🔹 Function for Visual Question Answering (VQA) with Mixtral-8x7B
def answer_question(text, image, temperature=0.0, max_tokens=1024):
    base64_string, file_format = pil_to_base64(image)

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": text},
                {"type": "image_url", "image_url": f"data:image/{file_format};base64,{base64_string}"}
            ]
        }
    ]
    
    chat_response = groq_client.chat.completions.create(
        model="mixtral-8x7b-32768",
        messages=messages,
        temperature=temperature,
        max_tokens=max_tokens
    )

    return chat_response.choices[0].message.content

# 🔹 Function to Generate Three Images (Multi-Output)
def generate_images(prompt):
    stop_event.clear()
    img1 = model1.predict(prompt)
    img2 = model2.predict(prompt)
    img3 = model3.predict(prompt)
    return img1, img2, img3

# 🔹 Clear All Fields
def clear_all():
    return "", None, "", None, None, None

# 🔹 Set up Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# 🎓 AI Tutor, VQA & Image Generation")

    # 🔹 Section 1: Visual Question Answering (Groq)
    gr.Markdown("## 🖼️ Visual Question Answering (Mixtral-8x7B)")
    with gr.Row():
        with gr.Column(scale=2):
            question = gr.Textbox(placeholder="Ask about the image...", lines=2)
            image = gr.Image(type="pil")
            with gr.Row():
                temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.0, step=0.1)
                max_tokens = gr.Slider(label="Max Tokens", minimum=128, maximum=2048, value=1024, step=128)
        
        with gr.Column(scale=3):
            output_text = gr.Textbox(lines=10, label="Mixtral VQA Response")
    
    with gr.Row():
        clear_btn = gr.Button("Clear", variant="secondary")
        submit_btn_vqa = gr.Button("Submit", variant="primary")

    # 🔹 Section 2: Image Generation (3 Outputs)
    gr.Markdown("## 🎨 AI-Generated Images (3 Variations)")
    with gr.Row():
        prompt = gr.Textbox(placeholder="Describe the image you want...", lines=2)
        generate_btn = gr.Button("Generate Images", variant="primary")

    with gr.Row():
        image1 = gr.Image(label="Image 1")
        image2 = gr.Image(label="Image 2")
        image3 = gr.Image(label="Image 3")

    # 🔹 VQA Processing
    submit_btn_vqa.click(
        fn=answer_question,
        inputs=[question, image, temperature, max_tokens],
        outputs=[output_text]
    )

    # 🔹 Image Generation Processing
    generate_btn.click(
        fn=generate_images,
        inputs=[prompt],
        outputs=[image1, image2, image3]
    )

    # 🔹 Clear All Inputs
    clear_btn.click(
        fn=clear_all,
        inputs=[],
        outputs=[question, image, output_text, image1, image2, image3]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)