Update app.py

app.py

@@ -1,13 +1,19 @@
-import gradio as gr
+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):
@@ -22,14 +28,41 @@ def convert_image_to_array(image_dir):
         print(f"Error : {e}")
         return None
 
-def predict(image):
+@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:
-        # Convert the image to an array
-        _, image_data = cv2.imencode('.jpg', image)
-        image_array = convert_image_to_array(image_data.tobytes())
+        image_path = 'crop_image2.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 "Invalid image"
+            return JSONResponse(content={"error": "Invalid image"}, status_code=400)
 
         # Normalize the image
         image_array = np.array(image_array, dtype=np.float16) / 255.0
@@ -41,19 +74,10 @@ def predict(image):
         prediction = model.predict(image_array)
         predicted_class = label_binarizer.inverse_transform(prediction)[0]
 
-        return predicted_class
+        return {"prediction": predicted_class}
     except Exception as e:
-        return str(e)
-
-# Create a Gradio interface
-iface = gr.Interface(
-    fn=predict, 
-    inputs=gr.inputs.Image(type="numpy", label="Upload an image"), 
-    outputs="text",
-    title="Image Classification",
-    description="Upload an image to classify it."
-)
-
-# Launch the interface
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+
 if __name__ == "__main__":
-    iface.launch()
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000)