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Dockerfile

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+FROM python:3.12.2-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app"]

README (1).md

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+---
+title: Banana Classification
+emoji: 🔥
+colorFrom: pink
+colorTo: pink
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from flask import Flask, request, jsonify
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+
+# Load the trained model
+model = tf.keras.models.load_model('banana_classification.h5')
+
+# Define class labels
+class_labels = ["overripe", "ripe", "rotten", "unripe"]
+
+# Define route for image classification
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'image' not in request.files:
+        return jsonify({'error': 'No image file provided'}), 400
+
+    try:
+        img_file = request.files['image']
+        img = Image.open(img_file)
+        img = img.resize((224, 224))  # Resize image to match model input size
+        img_array = np.array(img) / 255.0  # Normalize image array
+        print(img_array)
+
+        # Ensure image array has the correct shape
+        if img_array.ndim == 2:  # grayscale image
+            img_array = np.expand_dims(img_array, axis=-1)
+            img_array = np.repeat(img_array, 3, axis=-1)  # Convert grayscale to RGB
+        elif img_array.shape[-1] != 3:  # If not RGB, convert to RGB
+            img_array = img_array[..., :3]
+            
+        predictions = model.predict(np.expand_dims(img_array, axis=0))[0]
+
+        predicted_class_index = np.argmax(predictions)
+        predicted_class = class_labels[predicted_class_index]
+        confidence = predictions[predicted_class_index]
+
+        response = {
+            'predicted_class': predicted_class,
+            'confidence': float(confidence)
+        }
+        return jsonify(response)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

banana_classification.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:18c64389d7979df9aa47c568fe861259bc7870a741dbb74178517f4def00fd26
+size 136021864

requirements.txt

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+flask==3.1.0
+pillow==11.1.0
+numpy==2.0.2
+tensorflow==2.18.1
+flask-cors==5.0.1
+gunicorn==23.0.0