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Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+
+COPY requirements.txt /app/
+RUN pip3 install -r requirements.txt
+COPY . /app
+
+CMD flask run -h 0.0.0.0 -p 10000

app.py

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+from flask import Flask
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+import requests
+import io
+
+# Initialize the Flask application
+app = Flask(__name__)
+
+# Load the trained model
+model = tf.keras.models.load_model('./save_model.h5',compile=False)
+
+# Route for object detection
+@app.route('/detect-object/<id>', methods=['POST','GET'])
+def detect_pothole(id):
+    # Get the image file from the request
+    try :
+        image_file  =  io.BytesIO(requests.get(f"https://firebasestorage.googleapis.com/v0/b/miniproj-2f595.appspot.com/o/{id}.jpg?alt=media&token=eca9d563-f526-4d9f-b443-72eb653b30d0").content)
+
+        print(f"https://firebasestorage.googleapis.com/v0/b/miniproj-2f595.appspot.com/o/{id}.jpg?alt=media&token=eca9d563-f526-4d9f-b443-72eb653b30d0")
+
+        # Load and preprocess the image
+        image = Image.open(image_file)
+        image = image.resize((64, 64))
+        image = np.array(image)
+        image = image / 255.0
+        image = np.expand_dims(image, axis=0)
+
+        # Debug statements
+        print('Image shape:', image.shape)
+        print('Image data:', image)
+
+        # Make predictions
+        result = model.predict(image)
+
+        # Convert the prediction to a label
+        if result[0][0] == 1:
+            prediction = 'pothole'
+        else:
+            prediction = 'Normal'
+
+    except :
+        prediction = 'error'
+
+    # Return the prediction as a JSON response
+    response = {'prediction': prediction}
+
+    return response
+
+# Run the Flask application
+if __name__ == '__main__':
+    app.run()

requirements.txt

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+tensorflow
+Flask
+Pillow
+requests