Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,92 +1,119 @@
+# import streamlit as st
+# from PIL import Image
+# import time
+# from core.inference import load_deepfake_model
+# from ui.styles import apply_custom_styles
+# from ui.components import render_header, render_result, render_footer
+
+# st.set_page_config(
+#     page_title="Deepfake Image Detector",
+#     page_icon="🕵️",
+#     layout="centered",
+#     initial_sidebar_state="collapsed",
+# )
+
+# apply_custom_styles()
+
+# render_header()
+
+# if "classifier" not in st.session_state:
+#     with st.spinner("Initializing Deepfake Detection AI..."):
+#         try:
+#             st.session_state.classifier = load_deepfake_model()
+#             st.success("Model loaded successfully ✅")
+#         except Exception as e:
+#             st.error(f"Model loading failed: {e}")
+#             st.stop()
+
+# classifier = st.session_state.classifier
+
+# st.markdown("### 📤 Image Upload")
+
+# uploaded_file = st.file_uploader(
+#     "Select or Drag & Drop an Image (JPG, JPEG, PNG)",
+#     type=["jpg", "jpeg", "png"],
+#     label_visibility="collapsed"
+# )
+
+# if uploaded_file is not None:
+#     try:
+#         image = Image.open(uploaded_file).convert("RGB")
+#     except Exception as e:
+#         st.error(f"Image loading error: {e}")
+#         st.stop()
+
+#     col1, col2, col3 = st.columns([1, 2, 1])
+#     with col2:
+#         st.image(image, caption="Uploaded Image", use_container_width=True)
+
+#     if st.button("Analyze Image"):
+#         progress_bar = st.progress(0)
+#         status_text = st.empty()
+
+#         # Fake progress for UX
+#         status_text.text("Extracting image features...")
+#         for i in range(0, 50, 10):
+#             time.sleep(0.1)
+#             progress_bar.progress(i)
+
+#         status_text.text("Running Deep Neural Network Inference...")
+
+#         try:
+#             results = classifier(image)
+#         except Exception as e:
+#             st.error(f"Inference failed: {e}")
+#             st.stop()
+
+#         try:
+#             top_result = results[0]
+#             label = top_result['label'].lower()
+#             score = top_result['score']
+#         except Exception as e:
+#             st.error(f"Result processing error: {e}")
+#             st.stop()
+
+#         for i in range(50, 100, 10):
+#             time.sleep(0.1)
+#             progress_bar.progress(i)
+
+#         progress_bar.progress(100)
+#         status_text.text("Analysis Complete.")
+#         time.sleep(0.5)
+
+#         progress_bar.empty()
+#         status_text.empty()
+
+
+#         is_fake = "fake" in label
+#         confidence_percentage = round(score * 100, 2)
+
+#         render_result(is_fake, confidence_percentage)
+
+# render_footer()
+
+
 import streamlit as st
 from PIL import Image
-import time
 from core.inference import load_deepfake_model
-from ui.styles import apply_custom_styles
-from ui.components import render_header, render_result, render_footer
-
-st.set_page_config(
-    page_title="Deepfake Image Detector",
-    page_icon="🕵️",
-    layout="centered",
-    initial_sidebar_state="collapsed",
-)
-
-apply_custom_styles()
-
-render_header()
-
-if "classifier" not in st.session_state:
-    with st.spinner("Initializing Deepfake Detection AI..."):
-        try:
-            st.session_state.classifier = load_deepfake_model()
-            st.success("Model loaded successfully ✅")
-        except Exception as e:
-            st.error(f"Model loading failed: {e}")
-            st.stop()
-
-classifier = st.session_state.classifier
-
-st.markdown("### 📤 Image Upload")
-
-uploaded_file = st.file_uploader(
-    "Select or Drag & Drop an Image (JPG, JPEG, PNG)",
-    type=["jpg", "jpeg", "png"],
-    label_visibility="collapsed"
-)
-
-if uploaded_file is not None:
-    try:
-        image = Image.open(uploaded_file).convert("RGB")
-    except Exception as e:
-        st.error(f"Image loading error: {e}")
-        st.stop()
-
-    col1, col2, col3 = st.columns([1, 2, 1])
-    with col2:
-        st.image(image, caption="Uploaded Image", use_container_width=True)
-
-    if st.button("Analyze Image"):
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-
-        # Fake progress for UX
-        status_text.text("Extracting image features...")
-        for i in range(0, 50, 10):
-            time.sleep(0.1)
-            progress_bar.progress(i)
-
-        status_text.text("Running Deep Neural Network Inference...")
-
-        try:
-            results = classifier(image)
-        except Exception as e:
-            st.error(f"Inference failed: {e}")
-            st.stop()
-
-        try:
-            top_result = results[0]
-            label = top_result['label'].lower()
-            score = top_result['score']
-        except Exception as e:
-            st.error(f"Result processing error: {e}")
-            st.stop()
-
-        for i in range(50, 100, 10):
-            time.sleep(0.1)
-            progress_bar.progress(i)
-
-        progress_bar.progress(100)
-        status_text.text("Analysis Complete.")
-        time.sleep(0.5)
-
-        progress_bar.empty()
-        status_text.empty()
-
-
-        is_fake = "fake" in label
-        confidence_percentage = round(score * 100, 2)
-
-        render_result(is_fake, confidence_percentage)
-
-render_footer()
+
+st.set_option('server.enableXsrfProtection', False)
+st.set_option('server.enableCORS', False)
+
+st.title("Stable Upload Test")
+
+@st.cache_resource
+def get_model():
+    return load_deepfake_model()
+
+classifier = get_model()
+
+uploaded_file = st.file_uploader("Upload Image")
+
+if uploaded_file:
+    img = Image.open(uploaded_file)
+    st.image(img)
+
+    if st.button("Analyze"):
+        st.write("Running...")
+        result = classifier(img)
+        st.write(result)