app.py

import io
from PIL import Image
import torch
from flask import Flask, request, jsonify
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
app = Flask(__name__)
# Load the model and processors
model_name = "colt12/maxcushion"
try:
    print("Loading model...")
    model = VisionEncoderDecoderModel.from_pretrained(model_name)
    print("Model loaded successfully.")
    
    print("Loading feature extractor...")
    feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
    print("Feature extractor loaded successfully.")
    
    print("Loading tokenizer...")
    tokenizer = AutoTokenizer.from_pretrained("gpt2")
    print("Tokenizer loaded successfully.")
except Exception as e:
    print(f"Error loading model or processors: {str(e)}")
    raise
def predict(image_bytes):
    # Open the image using PIL
    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
    
    # Preprocess the image
    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
    # Generate the caption
    with torch.no_grad():
        output_ids = model.generate(pixel_values, max_length=50, num_return_sequences=1)
    generated_caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    
    return generated_caption
@app.route('/', methods=['GET'])
def home():
    return "Welcome to the Image Captioning API. Use the /predict endpoint to generate captions for images."
@app.route('/predict', methods=['POST'])
def run():
    if 'image' not in request.files:
        return jsonify({"error": "No image provided"}), 400
    
    image_file = request.files['image']
    
    try:
        image_bytes = image_file.read()
        result = predict(image_bytes)
        return jsonify({"caption": result})
    except Exception as e:
        return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
    app.run(host='0.0.0.0', port=5000)