Update app.py

app.py

@@ -1,85 +1,85 @@
-import os
-import gradio as gr
-import json
-
-from gradio_client import Client, handle_file
-backend = Client(os.getenv("BACKEND"), hf_token=os.getenv("TOKEN"))
-
-def detect(image):
-    result_text = backend.predict(
-        image=handle_file(image),
-        api_name="/detect"
-    )
-
-    result = json.loads(result_text)
-    if result and result["status"] == "ok":
-        return result["overall"], result["aigen"], result["deepfake"]
-    else:
-        raise gr.Error("Error in processing image")
-
-custom_css = """
-.button-gradient {
-  background: linear-gradient(45deg, #ff416c, #ff4b2b, #ff9b00, #ff416c);
-  background-size: 400% 400%;
-  border: none;
-  padding: 14px 28px;
-  font-size: 16px;
-  font-weight: bold;
-  color: white;
-  border-radius: 10px;
-  cursor: pointer;
-  transition: 0.3s ease-in-out;
-  animation: gradientAnimation 2s infinite linear;
-  box-shadow: 0 4px 10px rgba(255, 65, 108, 0.6);
-}
-
-@keyframes gradientAnimation {
-  0% { background-position: 0% 50%; }
-  25% { background-position: 50% 100%; }
-  50% { background-position: 100% 50%; }
-  75% { background-position: 50% 0%; }
-  100% { background-position: 0% 50%; }
-}
-
-.button-gradient:hover {
-  transform: scale(1.05);
-  box-shadow: 0 6px 15px rgba(255, 75, 43, 0.8);
-}
-"""
-
-MARKDOWN0 = """
-    # DeepFake Detector - ❤️Like above if this space helps
-    #### [Learn more about our solutions.](https://faceonlive.com)
-"""
-MARKDOWN3 = """
-<div align="right"><a href="https://faceonlive.com/face-search-online" target='_blank' style='font-size: 16px;'>Reverse Face Search</div><br/>
-<div align="right"><a href="https://faceonlive.com/reverse-image-search" target='_blank' style='font-size: 16px;'>Reverse Image Search</div>
-"""
-lbl_overall = gr.Label(label = "Overall")
-lbl_aigen = gr.Label(label = "Generative AI Model")
-lbl_deepfake = gr.Label(label = "Face Manipulation")
-
-with gr.Blocks(css=custom_css) as demo:
-    gr.Markdown(MARKDOWN0)
-    with gr.Row():
-        with gr.Column(scale=1) as col1:
-            image = gr.Image(type='filepath', height=360)
-            detect_button = gr.Button("Detect", elem_classes="button-gradient")
-            gr.Examples(['examples/1.jpg', 'examples/2.jpg'], inputs=image, cache_examples=True, fn=detect, outputs = [lbl_overall, lbl_aigen, lbl_deepfake])
-        with gr.Column(scale=2) as col2:
-            lbl_overall.render()
-            with gr.Row():
-                with gr.Column():
-                    lbl_aigen.render()
-                with gr.Column():
-                    lbl_deepfake.render()
-    gr.HTML(MARKDOWN3)
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.HTML('<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FFaceOnLive%2FDeep-Fake-Detector"><img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FFaceOnLive%2FDeep-Fake-Detector&labelColor=%23ff8a65&countColor=%2337d67a&style=flat&labelStyle=upper" /></a>')
-        with gr.Column(scale=5):
-            html = gr.HTML()
-
-    detect_button.click(detect, inputs=[image], outputs=[lbl_overall, lbl_aigen, lbl_deepfake], api_name=False)
-
+import os
+import gradio as gr
+import json
+
+from gradio_client import Client, handle_file
+backend = Client(os.getenv("BACKEND"), hf_token=os.getenv("TOKEN"))
+
+def detect(image):
+    result_text = backend.predict(
+        image=handle_file(image),
+        api_name="/detect"
+    )
+
+    result = json.loads(result_text)
+    if result and result["status"] == "ok":
+        return result["overall"], result["aigen"], result["deepfake"]
+    else:
+        raise gr.Error("Error in processing image")
+
+custom_css = """
+.button-gradient {
+  background: linear-gradient(45deg, #ff416c, #ff4b2b, #ff9b00, #ff416c);
+  background-size: 400% 400%;
+  border: none;
+  padding: 14px 28px;
+  font-size: 16px;
+  font-weight: bold;
+  color: white;
+  border-radius: 10px;
+  cursor: pointer;
+  transition: 0.3s ease-in-out;
+  animation: gradientAnimation 2s infinite linear;
+  box-shadow: 0 4px 10px rgba(255, 65, 108, 0.6);
+}
+
+@keyframes gradientAnimation {
+  0% { background-position: 0% 50%; }
+  25% { background-position: 50% 100%; }
+  50% { background-position: 100% 50%; }
+  75% { background-position: 50% 0%; }
+  100% { background-position: 0% 50%; }
+}
+
+.button-gradient:hover {
+  transform: scale(1.05);
+  box-shadow: 0 6px 15px rgba(255, 75, 43, 0.8);
+}
+"""
+
+MARKDOWN0 = """
+    # DeepFake Detector - ❤️Like above if this space helps
+    #### [Learn more about our Deepfake Detection.](https://faceonlive.com/deepfake-detector)
+"""
+MARKDOWN3 = """
+<div align="right"><a href="https://faceonlive.com/face-search-online" target='_blank' style='font-size: 16px;'>Reverse Face Search</div><br/>
+<div align="right"><a href="https://faceonlive.com/reverse-image-search" target='_blank' style='font-size: 16px;'>Reverse Image Search</div>
+"""
+lbl_overall = gr.Label(label = "Overall")
+lbl_aigen = gr.Label(label = "Generative AI Model")
+lbl_deepfake = gr.Label(label = "Face Manipulation")
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown(MARKDOWN0)
+    with gr.Row():
+        with gr.Column(scale=1) as col1:
+            image = gr.Image(type='filepath', height=360)
+            detect_button = gr.Button("Detect", elem_classes="button-gradient")
+            gr.Examples(['examples/1.jpg', 'examples/2.jpg'], inputs=image, cache_examples=True, fn=detect, outputs = [lbl_overall, lbl_aigen, lbl_deepfake])
+        with gr.Column(scale=2) as col2:
+            lbl_overall.render()
+            with gr.Row():
+                with gr.Column():
+                    lbl_aigen.render()
+                with gr.Column():
+                    lbl_deepfake.render()
+    gr.HTML(MARKDOWN3)
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.HTML('<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FFaceOnLive%2FDeep-Fake-Detector"><img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FFaceOnLive%2FDeep-Fake-Detector&labelColor=%23ff8a65&countColor=%2337d67a&style=flat&labelStyle=upper" /></a>')
+        with gr.Column(scale=5):
+            html = gr.HTML()
+
+    detect_button.click(detect, inputs=[image], outputs=[lbl_overall, lbl_aigen, lbl_deepfake], api_name=False)
+
 demo.queue(api_open=False, default_concurrency_limit=8).launch(server_name="0.0.0.0", show_api=False)