app.py

import gradio as gr
import spaces
from PIL import Image
import os
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
import subprocess
from io import BytesIO

# Install flash-attn
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)


# Load the model and processor
model_id = "microsoft/Phi-3.5-vision-instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    use_flash_attention_2=False,  # Explicitly disable Flash Attention 2
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True, num_crops=16)

@spaces.GPU(duration=120)
def solve_math_problem(image):
    # Move model to GPU for this function call
    model.to('cuda')
    
    # Prepare the input
    messages = [
        {"role": "user", "content": "<|image_1|>\nSolve this math problem step by step. Explain your reasoning clearly."},
    ]
    prompt = processor.tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    
    # Process the input
    inputs = processor(prompt, image, return_tensors="pt").to("cuda")
    
    # Generate the response
    generation_args = {
        "max_new_tokens": 1000,
        "temperature": 0.2,
        "do_sample": True,
    }
    generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args)
    
    # Decode the response
    generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
    response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    
    # Move model back to CPU to free up GPU memory
    model.to('cpu')
    return response

# Custom CSS
custom_css = """
<style>
    body {
        font-family: 'Arial', sans-serif;
        background-color: #f0f3f7;
        margin: 0;
        padding: 0;
    }
    .container {
        max-width: 1200px;
        margin: 0 auto;
        padding: 20px;
    }
    .header {
        background-color: #2c3e50;
        color: white;
        padding: 20px 0;
        text-align: center;
    }
    .header h1 {
        margin: 0;
        font-size: 2.5em;
    }
    .main-content {
        display: flex;
        justify-content: space-between;
        margin-top: 30px;
    }
    .input-section, .output-section {
        width: 48%;
        background-color: white;
        border-radius: 8px;
        padding: 20px;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    }
    .gr-button {
        background-color: #27ae60;
        color: white;
        border: none;
        padding: 10px 20px;
        border-radius: 5px;
        cursor: pointer;
        transition: background-color 0.3s;
    }
    .gr-button:hover {
        background-color: #2ecc71;
    }
    .examples-section {
        margin-top: 30px;
        background-color: white;
        border-radius: 8px;
        padding: 20px;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    }
    .examples-section h3 {
        margin-top: 0;
        color: #2c3e50;
    }
    .footer {
        text-align: center;
        margin-top: 30px;
        color: #7f8c8d;
    }
</style>
"""

# Create the Gradio interface
with gr.Blocks(css=custom_css) as iface:
    gr.HTML("""
        <div class="header">
            <h1>AI Math Equation Solver</h1>
            <p>Upload an image of a math problem, and our AI will solve it step by step!</p>
        </div>
    """)
    
    with gr.Row(equal_height=True):
        with gr.Column():
            gr.HTML("<h2>Upload Your Math Problem</h2>")
            input_image = gr.Image(type="pil", label="Upload Math Problem Image")
            submit_btn = gr.Button("Solve Problem", elem_classes=["gr-button"])
        
        with gr.Column():
            gr.HTML("<h2>Solution</h2>")
            output_text = gr.Textbox(label="Step-by-step Solution", lines=10)
    
    gr.HTML("<h3>Try These Examples</h3>")
    examples = gr.Examples(
        examples=[
            os.path.join(os.path.dirname(__file__), "eqn1.png"),
            os.path.join(os.path.dirname(__file__), "eqn2.png")
        ],
        inputs=input_image,
        outputs=output_text,
        fn=solve_math_problem,
        cache_examples=True,
    )
    
    gr.HTML("""
        <div class="footer">
            <p>Powered by Gradio and AI - Created for educational purposes</p>
        </div>
    """)

    submit_btn.click(fn=solve_math_problem, inputs=input_image, outputs=output_text)

# Launch the app
iface.launch()