add files

CNN_TEA_MODEL.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d6b5eef6d972efa8aa417dfc8f20873683c58e0189a9e35aac3121628aa13d49
+size 378166960

Dockerfile

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+FROM python:3.10.11
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir -r /code/requirements.txt
+
+COPY . .
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "sever:app"]

sever.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
+from PIL import Image
+import io
+import os
+import base64
+from flask_cors import CORS
+
+
+
+app = Flask(__name__)
+CORS(app)
+
+#dataset clasess
+class_name = {
+    0: 'Blister Blight',
+    1: 'Brown Blight',
+    2: 'Gray Blight',
+    3: 'Healthy',
+    4: 'White Spot'
+}
+
+#load saved model
+model_dir = './CNN_TEA_MODEL.h5'
+model = load_model(model_dir)
+
+
+    
+        
+
+@app.route("/predict", methods=["POST"])
+def predictTest():
+    
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file part'}), 400
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({'error': 'No selected file'}), 400
+    if file:
+        # Convert the file storage to PIL Image and ensure it's in RGB
+        img = Image.open(io.BytesIO(file.read())).convert('RGB')  # Added .convert('RGB')
+        img = img.resize((256, 256))
+        img_array = np.array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0  # Normalize
+        
+        predictions = model.predict(img_array)
+        #get the class with the highest probability
+        predicted_class = np.argmax(predictions, axis=1)
+        predicted_class_name = class_name[predicted_class[0]]
+        
+        result = {"class": predicted_class_name}
+        print("Prediction: ", result)
+        print(predictions)
+        
+        return jsonify({'prediction': result})
+
+
+
+
+
+
+if __name__ == "__main__":
+    app.run(debug=True)
+
+