Update app.py

app.py

@@ -1,17 +1,17 @@
-import streamlit as st
+import gradio as gr
 import requests
 import base64
 from PIL import Image
 import io
 
-st.set_page_config(page_title="AI Image Detector", page_icon="🔍")
+gr.set_page_config(page_title="AI Image Detector", page_icon="🔍")
 
-st.title("AI Image Detector")
-st.write("Upload an image to check if it's AI-generated")
+gr.title("AI Image Detector")
+gr.write("Upload an image to check if it's AI-generated")
 
 
 api_key = "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"
-st.session_state.api_key = api_key
+gr.session_state.api_key = api_key
 
 def process_image(image_bytes, api_key):
     header_auth = f"Bearer {api_key}"
@@ -44,20 +44,20 @@ def process_image(image_bytes, api_key):
                     'status': first_result.get('status', 'UNKNOWN')
                 }
         
-        st.error("Unexpected response format from API")
+        gr.error("Unexpected response format from API")
         return None
             
     except requests.exceptions.RequestException as e:
-        st.error(f"Error processing image: {str(e)}")
+        gr.error(f"Error processing image: {str(e)}")
         return None
 
 # File uploader
-uploaded_file = st.file_uploader("Choose an image...", type=['png', 'jpg', 'jpeg'])
+uploaded_file = gr.file_uploader("Choose an image...", type=['png', 'jpg', 'jpeg'])
 
 if uploaded_file is not None and api_key:
     # Display the uploaded image
     image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_container_width=True)
+    gr.image(image, caption="Uploaded Image", use_container_width=True)
     
     # Convert image to bytes
     img_byte_arr = io.BytesIO()
@@ -65,42 +65,42 @@ if uploaded_file is not None and api_key:
     img_byte_arr = img_byte_arr.getvalue()
     
     # Process the image
-    with st.spinner("Analyzing image..."):
+    with gr.spinner("Analyzing image..."):
         result = process_image(img_byte_arr, api_key)
         
         if result and result['status'] == 'SUCCESS':
             confidence = result['confidence']
             sources = result['sources']
             
-            st.write("---")
-            st.write("### Result")
+            gr.write("---")
+            gr.write("### Result")
             
             # Determine if image is AI-generated (using 50% threshold)
             is_ai_generated = "Yes" if confidence >= 0.5 else "No"
             
             # Display result with appropriate styling
             if is_ai_generated == "Yes":
-                st.error(f"Is this image AI-generated? **{is_ai_generated}**")
+                gr.error(f"Is this image AI-generated? **{is_ai_generated}**")
                 
                 # Show top 3 possible sources if AI-generated
                 if sources:
-                    st.write("Top possible AI models used:")
+                    gr.write("Top possible AI models used:")
                     sorted_sources = sorted(sources.items(), key=lambda x: x[1], reverse=True)[:3]
                     for source, prob in sorted_sources:
                         if prob > 0.01:  # Only show sources with >1% probability
-                            st.write(f"- {source}: {prob:.1%}")
+                            gr.write(f"- {source}: {prob:.1%}")
             else:
-                st.success(f"Is this image AI-generated? **{is_ai_generated}**")
+                gr.success(f"Is this image AI-generated? **{is_ai_generated}**")
             
             # Show confidence score in smaller text
-            st.caption(f"Confidence score: {confidence:.2%}")
+            gr.caption(f"Confidence score: {confidence:.2%}")
 
 elif not api_key and uploaded_file is not None:
-    st.warning("Please enter your NVIDIA API key first")
+    gr.warning("Please enter your NVIDIA API key first")
 
 # Add footer with instructions
-st.markdown("---")
-st.markdown("""
+gr.markdown("---")
+gr.markdown("""
 ---
 ### How to use: