done

Author: manikumarreddyu

combined-disease-detection-api/app.py

@@ -0,0 +1,83 @@
+from flask import Flask, request, jsonify
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes
+
+# Load TFLite Model and allocate tensors
+interpreter = tf.lite.Interpreter(model_path="plant_disease_model.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output tensors
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Define class names
+CLASS_NAMES = [
+    'Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy',
+    'Blueberry___healthy', 'Cherry_(including_sour)___Powdery_mildew', 
+    'Cherry_(including_sour)___healthy', 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot', 
+    'Corn_(maize)___Common_rust_', 'Corn_(maize)___Northern_Leaf_Blight', 'Corn_(maize)___healthy', 
+    'Grape___Black_rot', 'Grape___Esca_(Black_Measles)', 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 
+    'Grape___healthy', 'Orange___Haunglongbing_(Citrus_greening)', 'Peach___Bacterial_spot',
+    'Peach___healthy', 'Pepper,_bell___Bacterial_spot', 'Pepper,_bell___healthy', 
+    'Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy', 
+    'Raspberry___healthy', 'Soybean___healthy', 'Squash___Powdery_mildew', 
+    'Strawberry___Leaf_scorch', 'Strawberry___healthy', 'Tomato___Bacterial_spot', 
+    'Tomato___Early_blight', 'Tomato___Late_blight', 'Tomato___Leaf_Mold', 
+    'Tomato___Septoria_leaf_spot', 'Tomato___Spider_mites Two-spotted_spider_mite', 
+    'Tomato___Target_Spot', 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 'Tomato___Tomato_mosaic_virus',
+    'Tomato___healthy'
+]
+
+def model_prediction(image_bytes):
+    try:
+        # Load and preprocess the image
+        image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+        image = image.resize((224, 224))
+        input_arr = np.array(image)
+        input_arr = np.expand_dims(input_arr, axis=0).astype(np.float32) / 255.0
+
+        # Set the tensor for the input
+        interpreter.set_tensor(input_details[0]['index'], input_arr)
+
+        # Run the model
+        interpreter.invoke()
+
+        # Get the output tensor and return the index of the max element
+        output_data = interpreter.get_tensor(output_details[0]['index'])
+        return int(np.argmax(output_data))
+    except Exception as e:
+        print(f"Error in model_prediction: {e}")
+        return None
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image part in the request"}), 400
+
+    file = request.files['image']
+
+    if file.filename == '':
+        return jsonify({"error": "No image selected for uploading"}), 400
+
+    try:
+        img_bytes = file.read()
+        result_index = model_prediction(img_bytes)
+
+        if result_index is None:
+            return jsonify({"error": "Prediction failed"}), 500
+
+        class_name = CLASS_NAMES[result_index]
+        return jsonify({"prediction": class_name}), 200
+
+    except Exception as e:
+        print(f"Error in /predict: {e}")
+        return jsonify({"error": "An error occurred during prediction"}), 500
+
+if __name__ == '__main__':
+    app.run(debug=True)

combined-disease-detection-api/plant_disease_model.tflite

@@ -0,0 +1,6 @@
+flask
+tensorflow
+numpy
+Pillow
+flask-cors
+gunicorn

combined-disease-detection-api/requirements.txt

@@ -102,11 +102,13 @@ const TestimonialSlider = () => {
                   border: '1px solid rgba(207, 230, 207, 0.5)', // Light green border
                 }}
               >
-                <img
-                  src={testimonial.image}
-                  alt={`${testimonial.author}'s picture`}
-                  className="w-24 h-24 md:w-32 md:h-32 rounded-full mb-4 border-2 border-green-400 transition-all duration-300 transform hover:scale-105"
-                />
+                <div className="flex justify-center">
+                  <img
+                    src={testimonial.image}
+                    alt={`${testimonial.author}'s picture`}
+                    className="w-24 h-24 md:w-32 md:h-32 rounded-full mb-4 border-2 border-green-400 transition-all duration-300 transform hover:scale-105"
+                  />
+                </div>
                 <p className="text-lg md:text-xl italic mb-4 text-center text-green-800">{testimonial.quote}</p>
                 <h4 className="text-base md:text-lg text-green-600 font-semibold text-center">- {testimonial.author}</h4>
               </div>
@@ -130,6 +132,7 @@ const TestimonialSlider = () => {
         </div>
       </div>
     </section>
+
   );
 };
 

frontend/src/components/TestimonialSlider.jsx

@@ -33,7 +33,7 @@ const PaddyRecognition = () => {
     setLoading(true);
     try {
       const response = await axios.post(
-        "http://127.0.0.1:5000/submit_paddy",
+        "https://agrp-tech-ai-paddy-api.onrender.com/submit_paddy",
         formData,
         {
           headers: {