app.py

import gradio as gr
import os
import torch
from transformers import AutoProcessor, MllamaForConditionalGeneration
from PIL import Image
import spaces
import tempfile
import requests

# Check if we're running in a Hugging Face Space and if SPACES_ZERO_GPU is enabled
IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
IS_SPACE = os.environ.get("SPACE_ID", None) is not None

# Determine the device (GPU if available, else CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"

print(f"Using device: {device}")
print(f"Low memory mode: {LOW_MEMORY}")

# Get Hugging Face token from environment variables
HF_TOKEN = os.environ.get('HF_TOKEN')

# Load the model and processor
model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
model = MllamaForConditionalGeneration.from_pretrained(
    model_name,
    use_auth_token=HF_TOKEN,
    torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
    device_map="auto" if device == "cuda" else None,  # Use device mapping if CUDA is available
)

# Move the model to the appropriate device (GPU if available)
model.to(device)
processor = AutoProcessor.from_pretrained(model_name, use_auth_token=HF_TOKEN)

# @spaces.GPU  # Use the free GPU provided by Hugging Face Spaces
# def predict(image, text):
#     # Prepare the input messages
#     messages = [
#         {"role": "user", "content": [
#             {"type": "image"},  # Specify that an image is provided
#             {"type": "text", "text": text}  # Add the user-provided text input
#         ]}
#     ]
    
#     # Create the input text using the processor's chat template
#     input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
    
#     # Process the inputs and move to the appropriate device
#     inputs = processor(image, input_text, return_tensors="pt").to(device)
    
#     # Generate a response from the model
#     outputs = model.generate(**inputs, max_new_tokens=100)
    
#     # Decode the output to return the final response
#     response = processor.decode(outputs[0], skip_special_tokens=True)
#     return response

def extract_text_from_pdf(pdf_url):
    try:
        response = requests.get(pdf_url)
        response.raise_for_status()
        with tempfile.NamedTemporaryFile(delete=False) as temp_pdf:
            temp_pdf.write(response.content)
            temp_pdf_path = temp_pdf.name
        
        reader = PdfReader(temp_pdf_path)
        text = ""
        for page in reader.pages:
            text += page.extract_text()
        
        os.remove(temp_pdf_path)
        return text
    except Exception as e:
        raise ValueError(f"Error extracting text from PDF: {str(e)}")
# raise HTTPException(status_code=400, detail=f"Error extracting text from PDF: {str(e)}")

@spaces.GPU
def predict_text(text, url = 'https://arinsight.co/2024_FA_AEC_1200_GR1_GR2.pdf'):
    pdf_text = extract_text_from_pdf('https://arinsight.co/2024_FA_AEC_1200_GR1_GR2.pdf')
    text_combined = text + "\n\nExtracted Text from PDF:\n" + pdf_text
    # Prepare the input messages
    messages = [{"role": "user", "content": [{"type": "text", "text": text_combined}]}]
    
    # Create the input text using the processor's chat template
    input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
    
    # Process the inputs and move to the appropriate device
    # inputs = processor(image, input_text, return_tensors="pt").to(device)
    inputs = processor(text=input_text, return_tensors="pt").to("cuda")
    # Generate a response from the model
    outputs = model.generate(**inputs, max_new_tokens=1024)
    
    # Decode the output to return the final response
    response = processor.decode(outputs[0], skip_special_tokens=True)
    return response


# Define the Gradio interface
interface = gr.Interface(
    fn=predict_text,
    inputs=[
        # gr.Image(type="pil", label="Image Input"),  # Image input with label
        gr.Textbox(label="Text Input")  # Textbox input with label
    ],
    outputs=gr.Textbox(label="Generated Response"),  # Output with a more descriptive label
    title="Llama 3.2 11B Vision Instruct Demo",  # Title of the interface
    description="This demo uses Meta's Llama 3.2 11B Vision model to generate responses based on an image and text input.",  # Short description
    theme="compact"  # Using a compact theme for a cleaner look
)

# Launch the interface
interface.launch(debug=True)