Update app.py

app.py

@@ -30,22 +30,37 @@ def load_datasets():
 def load_image(image_file):
     return Image.open(image_file)
 
+
 def classify_image(image):
+    # Convert PIL Image to bytes
     img_byte_arr = BytesIO()
     image.save(img_byte_arr, format='PNG')
     img_byte_arr = img_byte_arr.getvalue()
 
-    headers = {"Authorization": f"Bearer {HUGGINGFACE_API_KEY}"}
-    response = requests.post(
-        'https://api-inference.huggingface.co/models/dima806/car_models_image_detection',
-        headers=headers,
-        files={"file": img_byte_arr}
-    )
+    # Encode image to base64
+    encoded_image = base64.b64encode(img_byte_arr).decode('ascii')
+
+    headers = {
+        "Authorization": f"Bearer {HUGGINGFACE_API_KEY}",
+        "Content-Type": "application/json"
+    }
     
-    if response.status_code == 200:
+    payload = {
+        "inputs": encoded_image
+    }
+
+    try:
+        response = requests.post(
+            'https://api-inference.huggingface.co/models/dima806/car_models_image_detection',
+            headers=headers,
+            json=payload
+        )
+        response.raise_for_status()  # Raises an HTTPError for bad responses
         return response.json()
-    else:
-        st.error(f"Image classification failed: {response.status_code} - {response.text}")
+    except requests.exceptions.RequestException as e:
+        st.error(f"Image classification failed: {e}")
+        if response.text:
+            st.error(f"API Response: {response.text}")
         return None
 
 def find_closest_match(df, brand, model):
@@ -116,10 +131,12 @@ camera_image = st.camera_input("Take a picture of the car!")
 
 if camera_image is not None:
     image = load_image(camera_image)
-    st.image(image, caption='Captured Image.', use_container_width=True)  # Updated parameter
+    st.image(image, caption='Captured Image.', use_container_width=True)
 
     # Classify the car image
-    car_info = classify_image(image)
+    with st.spinner('Classifying image...'):
+        car_info = classify_image(image)
+    
     if car_info:
         brand = car_info.get('brand', None)  # Adjust according to the response structure
         model_name = car_info.get('model', None)