app.py

# Retrieve the token from environment variables
#api_token = os.getenv("HF_TOKEN").strip()

import torch
from flask import Flask, request, jsonify
from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig
from PIL import Image
import io
import base64

app = Flask(__name__)

# Quantization configuration
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, 
    bnb_4bit_quant_type="nf4", 
    bnb_4bit_use_double_quant=True, 
    bnb_4bit_compute_dtype=torch.float16
)

# Load model
model = AutoModel.from_pretrained(
    "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1", 
    quantization_config=bnb_config, 
    device_map="auto", 
    torch_dtype=torch.float16, 
    trust_remote_code=True, 
    attn_implementation="flash_attention_2"
)

tokenizer = AutoTokenizer.from_pretrained(
    "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1", 
    trust_remote_code=True
)

def decode_base64_image(base64_string):
    # Decode base64 image
    image_data = base64.b64decode(base64_string)
    image = Image.open(io.BytesIO(image_data)).convert('RGB')
    return image

@app.route('/analyze', methods=['POST'])
def analyze_input():
    data = request.json
    question = data.get('question', '')
    base64_image = data.get('image', None)

    try:
        # Process with image if provided
        if base64_image:
            image = decode_base64_image(base64_image)
            inputs = model.prepare_inputs_for_generation(
                input_ids=tokenizer(question, return_tensors="pt").input_ids,
                images=[image]
            )
            outputs = model.generate(**inputs, max_new_tokens=256)
        else:
            # Text-only processing
            inputs = tokenizer(question, return_tensors="pt")
            outputs = model.generate(**inputs, max_new_tokens=256)

        # Decode response
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)

        return jsonify({
            'status': 'success', 
            'response': response
        })

    except Exception as e:
        return jsonify({
            'status': 'error', 
            'message': str(e)
        }), 500


if __name__ == '__main__':
    app.run(debug=True)