Update app.py

app.py

@@ -1,35 +1,53 @@
 import gradio as gr
-from models import text_model, image_model, text_label_map
-
-def classify_text(text):
-    res = text_model(text)[0]
-    # Map numeric labels to user-readable text
-    readable_label = text_label_map.get(res['label'], res['label'])
-    return f"{readable_label} ({res['score']*100:.2f}% confidence)"
+from models import text_model_1, text_label_map_1, text_model_2, text_label_map_2
+from models import image_model_1, image_label_map_1, image_model_2, image_label_map_2
+from models import video_model
 
+# Text classification function (combines two text models)
+def classify_text(input_text):
+    results = []
+    for model, label_map in [(text_model_1, text_label_map_1), (text_model_2, text_label_map_2)]:
+        res = model(input_text)[0]
+        label = label_map.get(res['label'], res['label'])
+        score = res['score'] * 100
+        results.append(f"{label} ({score:.2f}%)")
+    return "\n".join(results)
 
+# Image classification function (combines two image models)
 def classify_image(image):
-    res = image_model(image)[0]
-    return f"{res['label']} ({res['score']*100:.2f}% confidence)"
-
-# --- Gradio UI ---
-with gr.Blocks(title="Authenticity Checker") as demo:
-    gr.Markdown("## Real or Fake? (Text & Image Detector)")
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("### Fake News Detection (Text)")
-            text_input = gr.Textbox(lines=5, placeholder="Paste news text here...", label="Input Text")
-            text_output = gr.Textbox(label="Result")
-            text_button = gr.Button("Check Text")
-            text_button.click(classify_text, inputs=text_input, outputs=text_output)
-
-        with gr.Column():
-            gr.Markdown("### Deepfake Detection (Image)")
-            image_input = gr.Image(type="pil", label="Upload Image")
-            image_output = gr.Textbox(label="Result")
-            image_button = gr.Button("Check Image")
-            image_button.click(classify_image, inputs=image_input, outputs=image_output)
-
-if __name__ == "__main__":
-    demo.launch()
+    results = []
+    for model, label_map in [(image_model_1, image_label_map_1), (image_model_2, image_label_map_2)]:
+        res = model(image)[0]
+        label = label_map.get(res['label'], res['label'])
+        score = res['score'] * 100
+        results.append(f"{label} ({score:.2f}%)")
+    return "\n".join(results)
+
+# Video classification placeholder
+def classify_video(video_file):
+    return video_model(video_file)
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## Multi-Modal AI Detection Tool")
+
+    with gr.Tab("Text Detection"):
+        text_input = gr.Textbox(label="Enter text")
+        text_output = gr.Textbox(label="Predictions")
+        text_btn = gr.Button("Classify Text")
+        text_btn.click(classify_text, inputs=text_input, outputs=text_output)
+
+    with gr.Tab("Image Detection"):
+        image_input = gr.Image(type="pil")
+        image_output = gr.Textbox(label="Predictions")
+        image_btn = gr.Button("Classify Image")
+        image_btn.click(classify_image, inputs=image_input, outputs=image_output)
+
+    with gr.Tab("Video Detection"):
+        video_input = gr.File(label="Upload video")
+        video_output = gr.Textbox(label="Prediction")
+        video_btn = gr.Button("Classify Video")
+        video_btn.click(classify_video, inputs=video_input, outputs=video_output)
+
+demo.launch()