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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+yashwanth3(200e)_plantmodel.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,33 @@
+from flask import Flask, request, jsonify
+import tensorflow as tf
+import numpy as np
+import cv2
+import base64
+
+app = Flask(__name__)
+
+# Load the ML model
+model = tf.keras.models.load_model("model.h5")
+
+# Function to decode base64 image
+def decode_image(image_data):
+    image_bytes = base64.b64decode(image_data)
+    image_np = np.frombuffer(image_bytes, dtype=np.uint8)
+    image = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
+    image = cv2.resize(image, (224, 224))  # Adjust based on your model
+    image = image / 255.0  # Normalize if needed
+    return image.reshape(1, 224, 224, 3)
+
+# API endpoint for prediction
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        data = request.json['image']
+        image = decode_image(data)
+        prediction = model.predict(image).tolist()
+        return jsonify({'prediction': prediction})
+    except Exception as e:
+        return jsonify({'error': str(e)})
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

@@ -0,0 +1,5 @@
+fastapi==0.115.11
+uvicorn==0.34.0
+tensorflow==2.14.0
+numpy==1.24.0
+pillow==11.1.0

yashwanth3(200e)_plantmodel.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:730522c647f022f4144a52b524a6026fae6b7391098b671d2ce9fa02e9414b13
+size 39710843