api_server.py

from flask import Flask, request, jsonify
from flask_cors import CORS
import tensorflow as tf
import numpy as np
from PIL import Image
import io
import base64

app = Flask(__name__)
CORS(app)  # Enable CORS for mobile app

# Define image dimensions
IMG_HEIGHT = 150
IMG_WIDTH = 150

# All 70 class names
class_names = [
    'Algal Leaf Spot (Jackfruit)', 'Anthracnose (Mango)', 'Aphids (Cotton)',
    'Apple scab (Apple)', 'Bacterial Blight (Cotton)', 'Bacterial Canker (Mango)',
    'Bacterial Leaf Spot (Pumpkin)', 'Bacterial spot (Peach)', 'Bacterial spot (Pepper, bell)',
    'Bacterial spot (Tomato)', 'BacterialBlights (Sugarcane)', 'Black Rot (Cauliflower)',
    'Black Spot (Jackfruit)', 'Black rot (Apple)', 'Black rot (Grape)',
    'BrownSpot (Rice)', 'Cedar apple rust (Apple)', 'Cercospora leaf spot Gray leaf spot (Corn (maize))',
    'Common rust (Corn (maize))', 'Cutting Weevil (Mango)', 'Die Back (Mango)',
    'Downy Mildew (Pumpkin)', 'Early blight (Potato)', 'Early blight (Tomato)',
    'Esca (Black Measles) (Grape)', 'Gall Midge (Mango)', 'Haunglongbing (Citrus greening) (Orange)',
    'Healthy (Cauliflower)', 'Healthy (Cotton)', 'Healthy (Jackfruit)',
    'Healthy (Mango)', 'Healthy (Rice)', 'Healthy (Sugarcane)',
    'Healthy Leaf (Pumpkin)', 'Hispa (Rice)', 'Late blight (Potato)',
    'Late blight (Tomato)', 'Leaf Mold (Tomato)', 'Leaf blight (Isariopsis Leaf Spot) (Grape)',
    'Leaf scorch (Strawberry)', 'LeafBlast (Rice)', 'Mosaic (Sugarcane)',
    'Mosaic Disease (Pumpkin)', 'Northern Leaf Blight (Corn (maize))', 'Powdery Mildew (Cotton)',
    'Powdery Mildew (Mango)', 'Powdery Mildew (Pumpkin)', 'Powdery mildew (Cherry (including sour))',
    'RedRot (Sugarcane)', 'Rust (Sugarcane)', 'Septoria leaf spot (Tomato)',
    'Sooty Mould (Mango)', 'Spider mites Two-spotted spider mite (Tomato)', 'Target Spot (Tomato)',
    'Target spot (Cotton)', 'Tomato Yellow Leaf Curl Virus (Tomato)', 'Tomato mosaic virus (Tomato)',
    'Unknown Disease', 'Yellow (Sugarcane)', 'healthy (Apple)',
    'healthy (Blueberry)', 'healthy (Cherry (including sour))', 'healthy (Corn (maize))',
    'healthy (Grape)', 'healthy (Peach)', 'healthy (Pepper, bell)',
    'healthy (Potato)', 'healthy (Raspberry)', 'healthy (Soybean)',
    'healthy (Strawberry)', 'healthy (Tomato)'
]

# Load model
print("Loading model...")
model = tf.saved_model.load('./plant_disease_savemodel')
infer = model.signatures["serving_default"]
print("✅ Model loaded successfully")

@app.route('/health', methods=['GET'])
def health():
    return jsonify({"status": "healthy", "model_loaded": True})

@app.route('/predict', methods=['POST'])
def predict():
    try:
        data = request.get_json()
        
        # Get base64 image from request
        image_data = data.get('image')
        if not image_data:
            return jsonify({"error": "No image provided"}), 400
        
        # Remove data URL prefix if present
        if ',' in image_data:
            image_data = image_data.split(',')[1]
        
        # Decode base64 image
        image_bytes = base64.b64decode(image_data)
        img = Image.open(io.BytesIO(image_bytes))
        
        # Ensure RGB mode
        if img.mode != 'RGB':
            img = img.convert('RGB')
        
        # Resize to model input size
        img = img.resize((IMG_WIDTH, IMG_HEIGHT))
        
        # Convert to array and normalize
        img_array = np.array(img, dtype=np.float32)
        img_array = img_array / 255.0
        
        # Add batch dimension
        img_array = np.expand_dims(img_array, axis=0)
        
        # Make prediction
        predictions = infer(tf.constant(img_array))
        
        # Get the output tensor
        if 'output_0' in predictions:
            output = predictions['output_0'].numpy()
        elif 'dense_1' in predictions:
            output = predictions['dense_1'].numpy()
        elif 'dense' in predictions:
            output = predictions['dense'].numpy()
        else:
            output = list(predictions.values())[0].numpy()
        
        # Create predictions dictionary
        predictions_dict = {}
        for i, class_name in enumerate(class_names):
            if i < len(output[0]):
                predictions_dict[class_name] = float(output[0][i])
        
        # Get top prediction
        top_class = max(predictions_dict.items(), key=lambda x: x[1])
        
        print(f"Prediction: {top_class[0]} ({top_class[1]*100:.2f}%)")
        
        return jsonify({
            "success": True,
            "predictions": predictions_dict,
            "top_prediction": {
                "class": top_class[0],
                "confidence": float(top_class[1])
            }
        })
        
    except Exception as e:
        print(f"Error: {str(e)}")
        return jsonify({"error": str(e)}), 500

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860)