Update app.py

app.py

@@ -1,96 +1,150 @@
 import gradio as gr
+
+# ---- IMPORT BACKENDS ----
 from image_backend import predict_image_pil
 from audio_backend import predict_audio
+# =========================
+
 
 # =========================
-# IMAGE LOGIC
+# IMAGE LOGIC (UNCHANGED)
 # =========================
 def analyze_image(image):
     label, confidence, heatmap = predict_image_pil(image)
+
     if label == "Fake":
         if confidence >= 90:
-            risk = "High likelihood of Deepfake"
+            risk = '<span class="material-icons">error</span> High likelihood of deepfake'
         elif confidence >= 60:
-            risk = "Possibly Deepfake"
+            risk = '<span class="material-icons">warning</span> Possibly deepfake'
         else:
-            risk = "Uncertain Deepfake"
+            risk = '<span class="material-icons">help_outline</span> Uncertain deepfake'
     else:
         if confidence >= 90:
-            risk = "Likely Real"
+            risk = '<span class="material-icons">check_circle</span> Likely real'
         elif confidence >= 60:
-            risk = "Possibly Real"
+            risk = '<span class="material-icons">warning</span> Possibly real'
         else:
-            risk = "Uncertain – Needs Review"
+            risk = '<span class="material-icons">help_outline</span> Uncertain – needs review'
+
     return label, f"{confidence} %", risk, heatmap
 
+
 # =========================
-# AUDIO LOGIC
+# AUDIO LOGIC (UNCHANGED)
 # =========================
 def analyze_audio(audio_path):
     label, confidence = predict_audio(audio_path)
+
     if label == "fake":
         if confidence >= 90:
-            risk = "High likelihood of Deepfake"
+            risk = '<span class="material-icons">error</span> High likelihood of deepfake'
         elif confidence >= 60:
-            risk = "Possibly Deepfake"
+            risk = '<span class="material-icons">warning</span> Possibly deepfake'
         else:
-            risk = "Uncertain – Needs Review"
+            risk = '<span class="material-icons">help_outline</span> Uncertain – needs review'
     else:
         if confidence >= 90:
-            risk = "Likely Real"
+            risk = '<span class="material-icons">check_circle</span> Likely real'
         elif confidence >= 60:
-            risk = "Possibly Real"
+            risk = '<span class="material-icons">warning</span> Possibly real'
         else:
-            risk = "Uncertain – Needs Review"
+            risk = '<span class="material-icons">help_outline</span> Uncertain – needs review'
+
     return label.capitalize(), f"{confidence} %", risk
 
+
 # =========================
 # UI
 # =========================
 with gr.Blocks(css="style.css") as demo:
-    # Load Material Icons stylesheet
-    gr.HTML('<link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">')
 
-    gr.Markdown("# AI Driven Deepfake Detection System")
+    # Load Material Icons
+    gr.Markdown("""
+    <link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
+    """)
+
+    gr.Markdown("# Unified Deepfake Detection System")
 
     with gr.Tabs():
+
         # HOME TAB
-        with gr.Tab('<span class="material-icons">home</span> Home'):
-            gr.Markdown("## Welcome\nSelect the type of media you want to analyze:")
+        with gr.Tab("Home"):
+            gr.Markdown("""
+            ## Welcome  
+            Select the type of media you want to analyze.
+            """)
+            gr.Markdown("""
+            **Detection Modes Available**
+            - Image Deepfake Detection  
+            - Audio Deepfake Detection  
+            """)
+            gr.Markdown("Use the tabs above to proceed.")
 
         # IMAGE TAB
-        with gr.Tab('<span class="material-icons">image</span> Image Detection'):
-            gr.Markdown("## Deepfake Image Detection System")
+        with gr.Tab("Image Deepfake"):
+            gr.Markdown("## Deepfake Image Detection")
+
             with gr.Row():
                 with gr.Column(scale=1):
-                    image_input = gr.Image(label="Upload Image", type="pil", height=280)
+                    image_input = gr.Image(
+                        label="Upload Image",
+                        type="pil",
+                        height=280
+                    )
                     img_submit = gr.Button("Submit")
                     img_clear = gr.Button("Clear")
+
                 with gr.Column(scale=2):
                     img_pred = gr.Text(label="Prediction")
                     img_conf = gr.Text(label="Confidence")
-                    img_risk = gr.Text(label="Risk Assessment")
-                    img_heatmap = gr.Image(label="Explainability Heatmap", height=280)
-            img_submit.click(fn=analyze_image, inputs=image_input,
-                             outputs=[img_pred, img_conf, img_risk, img_heatmap])
-            img_clear.click(fn=lambda: (None, "", "", None), inputs=None,
-                            outputs=[image_input, img_pred, img_conf, img_risk])
+                    img_risk = gr.HTML(label="Risk Assessment")
+                    img_heatmap = gr.Image(
+                        label="Explainability Heatmap",
+                        height=280
+                    )
+
+            img_submit.click(
+                fn=analyze_image,
+                inputs=image_input,
+                outputs=[img_pred, img_conf, img_risk, img_heatmap]
+            )
+
+            img_clear.click(
+                fn=lambda: (None, "", "", None),
+                inputs=None,
+                outputs=[image_input, img_pred, img_conf, img_risk]
+            )
 
         # AUDIO TAB
-        with gr.Tab('<span class="material-icons">audiotrack</span> Audio Detection'):
-            gr.Markdown("## Deepfake Audio Detection System")
+        with gr.Tab("Audio Deepfake"):
+            gr.Markdown("## Deepfake Audio Detection")
+
             with gr.Row():
                 with gr.Column(scale=1):
-                    audio_input = gr.Audio(label="Upload Audio (.wav)", type="filepath")
+                    audio_input = gr.Audio(
+                        label="Upload Audio (.wav)",
+                        type="filepath"
+                    )
                     aud_submit = gr.Button("Submit")
                     aud_clear = gr.Button("Clear")
+
                 with gr.Column(scale=2):
                     aud_pred = gr.Text(label="Prediction")
                     aud_conf = gr.Text(label="Confidence")
-                    aud_risk = gr.Text(label="Risk Assessment")
-            aud_submit.click(fn=analyze_audio, inputs=audio_input,
-                             outputs=[aud_pred, aud_conf, aud_risk])
-            aud_clear.click(fn=lambda: (None, "", ""), inputs=None,
-                            outputs=[audio_input, aud_pred, aud_conf])
+                    aud_risk = gr.HTML(label="Risk Assessment")
+
+            aud_submit.click(
+                fn=analyze_audio,
+                inputs=audio_input,
+                outputs=[aud_pred, aud_conf, aud_risk]
+            )
+
+            aud_clear.click(
+                fn=lambda: (None, "", ""),
+                inputs=None,
+                outputs=[audio_input, aud_pred, aud_conf]
+            )
+
 
-demo.launch()
+demo.launch()