app.py

from flask import Flask, request, jsonify
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
import io
import os
import requests
from flask_cors import CORS

app = Flask(_name_)
CORS(app)

# Hugging Face .h5 model URL
MODEL_URL = "https://huggingface.co/vishwak1/plant/resolve/main/model.h5"
MODEL_PATH = "model.h5"

# Download the model if it doesn't exist
if not os.path.exists(MODEL_PATH):
    print("Downloading model from Hugging Face...")
    response = requests.get(MODEL_URL)
    with open(MODEL_PATH, 'wb') as f:
        f.write(response.content)
    print("Model downloaded successfully.")

# Load the trained model
model = load_model(MODEL_PATH)

@app.route('/predict', methods=['POST'])
def predict():
    if 'file' not in request.files:
        return jsonify({'error': 'No file part'}), 400

    file = request.files['file']
    if file.filename == '':
        return jsonify({'error': 'No selected file'}), 400

    try:
        image_bytes = file.read()
        image = load_img(io.BytesIO(image_bytes), target_size=(225, 225))  # Match model input size

        x = img_to_array(image)
        x = x.astype('float32') / 255.0
        x = np.expand_dims(x, axis=0)

        predictions = model.predict(x)
        predicted_class = int(np.argmax(predictions, axis=1)[0])

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

if _name_ == '_main_':
    app.run(debug=True)