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app.py

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+import numpy as np
+import cv2
+from ultralytics import YOLO
+from flask import Flask, request, jsonify
+from flask_cors import CORS  # CORS for frontend access
+from PIL import Image
+import io
+
+# Initialize Flask App
+app = Flask("Plant Disease Detection")
+CORS(app)  # Allow frontend requests from any origin
+
+# Load YOLO Model
+model = YOLO('crop_disease_model.pt')
+
+# Disease Remedies Dictionary
+disease_remedies = {
+    "bacterial spot": "Remove infected plant debris, use copper-based fungicides.",
+    "early blight": "Apply fungicides, practice crop rotation.",
+    "healthy": "No action needed.",
+    "late blight": "Remove infected plants, use fungicides.",
+    "leaf miner": "Use insecticidal sprays, remove affected leaves.",
+    "leaf mold": "Improve air circulation, use fungicides.",
+    "mosaic virus": "Remove infected plants, control aphids.",
+    "septoria": "Remove infected leaves, use fungicides.",
+    "spider mites": "Use miticides, introduce beneficial insects.",
+    "yellow leaf curl virus": "Remove infected plants, control whiteflies."
+}
+
+# Function to process image
+def process_image(image):
+    img = cv2.resize(image, (512, 512))  # Resize to match model input
+    return img
+
+# Function for disease detection
+def plant_disease_detect(img):
+    detect_result = model(img)
+    detect_img = detect_result[0].plot()
+    detections = detect_result[0].boxes.data.tolist()
+    classes = [model.names[int(detection[5])] for detection in detections]
+    return detect_img, classes
+
+# Flask API Endpoint
+@app.route("/predict", methods=["GET", "POST"])
+def predict():
+    if request.method == "GET":
+        return jsonify({"message": "Use POST request to send an image for prediction."}), 400
+
+    if "file" not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+
+    file = request.files["file"]
+    image = Image.open(io.BytesIO(file.read())).convert("RGB")
+    image = np.array(image)
+    original_size = (image.shape[1], image.shape[0])
+
+    # Process image & detect disease
+    processed_img = process_image(image)
+    detect_img, classes = plant_disease_detect(processed_img)
+    
+    # Get unique classes with remedies
+    unique_classes = list(set(classes))
+    class_table = [{"disease": cls, "remedy": disease_remedies.get(cls.lower(), "No remedy available")} for cls in unique_classes]
+
+    return jsonify({"detections": class_table})
+
+# Home Route
+@app.route("/", methods=["GET"])
+def home():
+    return jsonify({"message": "Welcome to the Plant Disease Detection API!"})
+
+# Run Flask App
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000, debug=True)

crop_disease_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f91ab640f21b218dd2da3c0d39145ad658718c67a912b3e4da46e1b3ade53d63
+size 93867804

requirements.txt

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+torch
+ultralytics
+gradio
+opencv-python