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ReadMe.txt

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+- cnn_model.h5 and label_transform.pkl is Model File.
+
+- main.py is to needed to run to start the server.
+- in main.py, "/test-predict" is for testing purpose, "/predict" endpoint is for uploading image. 
+- "/test-predict" endpoint contains image picked from the same folder.
+- after starting the server, call "http://0.0.0.0:8000/test-predict"in browser to test the prediction on the image.

cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:68953b84edbf58393bdb203c7a08062d4367382883025f5b729d607e968cffeb
+size 697340252

label_transform.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1322197d23b302375c2f8a75a8e6954375a2241af686fa40d02be8f4f1bd07b
+size 2906

main.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+import numpy as np
+import cv2
+import pickle
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+
+app = FastAPI()
+
+print("app run")
+# Load the model and the label binarizer
+model = load_model('cnn_model.h5')
+print("model loaded")
+label_binarizer = pickle.load(open('label_transform.pkl', 'rb'))
+print("labels loaded")
+
+# Function to convert images to array
+def convert_image_to_array(image_dir):
+    try:
+        image = cv2.imdecode(np.frombuffer(image_dir, np.uint8), cv2.IMREAD_COLOR)
+        if image is not None:
+            image = cv2.resize(image, (256, 256))   
+            return img_to_array(image)
+        else:
+            return np.array([])
+    except Exception as e:
+        print(f"Error : {e}")
+        return None
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    try:
+        # Read the file and convert it to an array
+        image_data = await file.read()
+        image_array = convert_image_to_array(image_data)
+
+        if image_array.size == 0:
+            return JSONResponse(content={"error": "Invalid image"}, status_code=400)
+
+        # Normalize the image
+        image_array = np.array(image_array, dtype=np.float16) / 255.0
+
+        # Ensure the image_array has the correct shape (1, 256, 256, 3)
+        image_array = np.expand_dims(image_array, axis=0)
+
+        # Make a prediction
+        prediction = model.predict(image_array)
+        predicted_class = label_binarizer.inverse_transform(prediction)[0]
+
+        return {"prediction": predicted_class}
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+
+# Add a test GET endpoint to manually trigger the prediction
+@app.get("/test-predict")
+def test_predict():
+    try:
+        image_path = 'crop_image1.jpg'
+        image = cv2.imread(image_path)
+        image_array = cv2.resize(image, (256, 256))
+        image_array = img_to_array(image_array)
+
+        if image_array.size == 0:
+            return JSONResponse(content={"error": "Invalid image"}, status_code=400)
+
+        # Normalize the image
+        image_array = np.array(image_array, dtype=np.float16) / 255.0
+
+        # Ensure the image_array has the correct shape (1, 256, 256, 3)
+        image_array = np.expand_dims(image_array, axis=0)
+
+        # Make a prediction
+        prediction = model.predict(image_array)
+        predicted_class = label_binarizer.inverse_transform(prediction)[0]
+
+        return {"prediction": predicted_class}
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000)