Update app.py

app.py

@@ -3,13 +3,15 @@ import requests
 
 # Hugging Face Inference API URL and Token
 API_URL = "https://api-inference.huggingface.co/models/Organika/sdxl-detector"
-API_TOKEN = st.secrets["HF_API_TOKEN"]  # You'll store this in the Hugging Face secret
+API_TOKEN = st.secrets["HF_API_TOKEN"]
 
 headers = {"Authorization": f"Bearer {API_TOKEN}"}
 
 # Function to query the model
 def query(image_bytes):
-    response = requests.post(API_URL, headers=headers, files={"inputs": image_bytes})
+    # Prepare the payload with binary image data
+    files = {"inputs": image_bytes}
+    response = requests.post(API_URL, headers=headers, files=files)
     return response.json()
 
 # Streamlit UI
@@ -17,18 +19,21 @@ st.title("AI Image Detector")
 
 st.write("Upload an image, and we will check if it is AI-generated using the Hugging Face SDXL detector.")
 
-# File uploader for user to upload image
+# File uploader for the user to upload an image
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
     # Display the uploaded image
     st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-    
+
     st.write("Classifying...")
-    
+
+    # Read the uploaded file as bytes
+    image_bytes = uploaded_file.read()  # Read the file as bytes
+
     # Send the image to the model
-    result = query(uploaded_file)  # Use the file object directly
-    
+    result = query(image_bytes)
+
     # Display the result
     if "error" in result:
         st.error(f"Error: {result['error']}")