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.gitattributes

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-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
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-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
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-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+best_model.keras filter=lfs diff=lfs merge=lfs -text

.gitignore

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+myenv

app.py

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+from flask import Flask, request, jsonify
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import io
+from PIL import Image
+import os
+import requests
+
+# Constants
+MODEL_URL = "https://storage.googleapis.com/potato-model-detection/potato_disease_model.h5"
+MODEL_PATH = "best_model.keras"
+
+# Download model if not already present
+def download_model():
+    if not os.path.exists(MODEL_PATH):
+        print("Downloading model...")
+        response = requests.get(MODEL_URL)
+        if response.status_code == 200:
+            with open(MODEL_PATH, 'wb') as f:
+                f.write(response.content)
+            print("Model downloaded successfully.")
+        else:
+            raise Exception(f"Failed to download model: {response.status_code}")
+
+# Call download before loading the model
+download_model()
+model = load_model(MODEL_PATH)
+
+class_names = ["Early Blight", "Late Blight", "Healthy"]
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Image preprocessing function
+def preprocess_image(img_bytes):
+    img = Image.open(io.BytesIO(img_bytes)).resize((256, 256))
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file part in request"}), 400
+
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({"error": "No file selected"}), 400
+
+    try:
+        img_bytes = file.read()
+        processed = preprocess_image(img_bytes)
+        prediction = model.predict(processed)
+        class_index = np.argmax(prediction[0])
+        result = class_names[class_index]
+        return jsonify({"prediction": result})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/", methods=["GET"])
+def home():
+    return "Potato Disease Detection API is running!"
+
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=5000)

best_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d77e7226bd2f7b1197737985b914d50273b8138f0fb8aba3794bb97f756395d
+size 47916878