Update app.py

app.py

@@ -1,56 +1,83 @@
-import gradio as gr
-import numpy as np
-import cv2
-import pickle
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array
-
-# Load the model and the label binarizer
-model = load_model('cnn_model.h5')
-label_binarizer = pickle.load(open('label_transform.pkl', 'rb'))
-
-# Function to convert images to array
-def convert_image_to_array(image):
-    try:
-        image = cv2.imdecode(np.frombuffer(image, 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
-
-def predict_image(image):
-    try:
-        image_array = convert_image_to_array(image)
-
-        if image_array.size == 0:
-            return "Invalid image"
-
-        # 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 predicted_class
-    except Exception as e:
-        return str(e)
-
-# Define Gradio interface
-interface = gr.Interface(
-    fn=predict_image, 
-    inputs=gr.Image(type="numpy"), 
-    outputs="text",
-    title="Image Classification",
-    description="Upload an image to get the predicted class."
-)
-
-if __name__ == "__main__":
-    interface.launch(share=True)
+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)