using wsgi which is gunicorn

Dockerfile

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+FROM python:3.9
+
+COPY requirements.txt .
+
+RUN pip install -r requirements.txt
+
+RUN apt-get update && apt-get install -y libgl1-mesa-glx
+
+RUN python -c "import cv2; import numpy"
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app"]

app.py

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+from flask import Flask, request
+# from io import BytesIO
+# from PIL import Image
+# import treeCountModel
+import recycleModel
+import numpy as np
+# import requests
+import cv2
+
+app = Flask(__name__)
+
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    message = "Welcome to my API! Here are the available endpoints:"
+    recycle_url = request.host_url + "recycle-prediction"
+    tree_count_url = request.host_url + "tree-count"
+    response = {
+        "message": message,
+        "endpoints": {"recycle_prediction": recycle_url, "tree_count": tree_count_url},
+    }
+    return response, 200
+
+
+@app.route("/recycle-prediction", methods=["GET", "POST"])
+def recycle():
+    result = None
+    if request.method == "POST":
+        if "img" not in request.files:
+            return "No file uploaded for recycling material prediction", 400
+        img = request.files["img"]
+        img_array = cv2.imdecode(np.frombuffer(img.read(), np.uint8), cv2.IMREAD_COLOR)
+        model = recycleModel.recyclePrediction(img_array)
+        # model = recycleModel.recyclePrediction(img)
+        response = {"Recycling Material": model}
+        result = response
+        return result
+
+    return result
+
+
+# @app.route("/tree-count/<path:url>", methods=["GET", "POST"])
+# def treeCount(url):
+#     result = None
+#     if request.method == "GET":
+#         url = request.url
+#         url = url[50:]
+#         response = requests.get(url)
+#         if url is None:
+#             return "No image URL provided", 400
+#         img = Image.open(BytesIO(response.content))
+#         model = treeCountModel.countTree(img)
+#         result = {"Tree Count": model}
+
+#     return result
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2470e83e4ca362c2834c3b964698702cd08cb183e9649b359abe9cd9750f80cd
+size 14307901

recycleModel.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Flatten
+
+class RecycleModel:
+    def __init__(self):
+        self.vgg16_model = Sequential()
+
+        self.pretrained_model = tf.keras.applications.VGG16(
+            include_top=False,
+            weights="imagenet",
+            input_shape=(180, 180, 3),
+            pooling='max',
+            classes=7
+        )
+        for layer in self.pretrained_model.layers:
+            layer.trainable = False
+
+        self.vgg16_model.add(self.pretrained_model)
+        self.vgg16_model.add(Flatten())
+        self.vgg16_model.add(Dense(512, activation='relu'))
+        self.vgg16_model.add(Dense(7, activation='softmax'))
+        
+        self.vgg16_model.compile(optimizer=Adam(learning_rate=0.001), 
+                                 loss='sparse_categorical_crossentropy', 
+                                 metrics=['accuracy'])
+
+        self.vgg16_model.load_weights('updated_vgg16_weights.h5')
+        
+        self.class_names = ['1_polyethylene_PET', '2_high_density_polyethylene_PE-HD', '3_polyvinylchloride_PVC',
+                            '4_low_density_polyethylene_PE-LD', '5_polypropylene_PP', '6_polystyrene_PS', '8_no_plastic']
+        
+    def recyclePrediction(self, img):
+        image_resized = cv2.resize(img, (180, 180))
+        image = np.expand_dims(image_resized, axis=0)
+        pred = self.vgg16_model.predict(image)
+        output_class = self.class_names[np.argmax(pred)]
+        
+        return output_class

updated_vgg16_weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9270ef02337952414ac31cfee42ef64746376d77de8b25139117304680d393ea
+size 59962752