Update app.py

app.py

@@ -4,14 +4,14 @@ from PIL import Image
 import random
 import traceback
 
-# Load all models
+# Load models
 models = {
     "Ateeq": pipeline("image-classification", model="ateeqahamed-ai/ai-image-detector"),
     "HuggingFace": pipeline("image-classification", model="umm-maybe/AI-image-detector"),
     "Falconsai": pipeline("image-classification", model="falconsai/nsfw_image_detection"),
 }
 
-# Normalize and balance confidence dynamically
+# Confidence normalization
 def normalize_confidence(label, confidence):
     if label.lower() in ["ai-generated", "fake", "ai generated", "ai", "generated"]:
         min_c, max_c = 0.75, 0.95
@@ -22,8 +22,7 @@ def normalize_confidence(label, confidence):
     adjusted = max(min_c, min(adjusted, max_c))
     return round(adjusted * 100, 2)
 
-
-# Combine model results with priority weighting
+# Combine model predictions with priority weighting
 def combine_results(results):
     weights = {"Ateeq": 0.6, "HuggingFace": 0.2, "Falconsai": 0.2}
     combined_scores = {"AI-generated": 0, "Real": 0}
@@ -37,8 +36,7 @@ def combine_results(results):
     final_score = combined_scores[final_label]
     return {"label": final_label, "score": final_score}
 
-
-# Main predict function
+# Main prediction logic
 def predict_image(image: Image.Image):
     try:
         results = {name: model(image) for name, model in models.items()}
@@ -47,52 +45,42 @@ def predict_image(image: Image.Image):
         label = combined["label"]
         score = normalize_confidence(label, combined["score"])
 
-        return f"Result: {label}\nConfidence: {score}%"
-
+        return f"🧠 {label}\nConfidence: {score}%"
     except Exception as e:
         traceback.print_exc()
-        return f"Error occurred: {str(e)}"
+        return f"❌ Error: {str(e)}"
 
-
-# Build Gradio Interface
+# UI
 with gr.Blocks(css="""
-    #loading {
-        display: none;
+    #result-box {
         text-align: center;
-        font-weight: bold;
-        color: #ff4444;
-        font-size: 18px;
-    }
-    #result {
-        text-align: center;
-        font-size: 20px;
-        font-weight: bold;
+        font-size: 22px;
+        font-weight: 600;
+        color: #222;
+        background: #f0f6ff;
+        border-radius: 12px;
+        padding: 15px;
+        box-shadow: 0px 0px 8px #a0c4ff;
         margin-top: 15px;
     }
 """) as demo:
-    gr.Markdown("<h1 style='text-align:center;'>🧠 UnrealEye AI Image Detector</h1>")
-    
-    with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload Image", elem_id="input_img")
-    
+    gr.Markdown("<h1 style='text-align:center; color:#007bff;'>🧠 UnrealEye AI Image Detector</h1>")
+
     with gr.Row():
-        analyze_btn = gr.Button("🔍 Analyse")
-        clear_btn = gr.Button("🧹 Clear")
+        image_input = gr.Image(type="pil", label="Upload Image")
 
-    loading = gr.Markdown("Analysing... Please wait ⚙️", elem_id="loading")
-    output_text = gr.Textbox(label="Detection Result", elem_id="result")
+    with gr.Row():
+        analyze_btn = gr.Button("🔍 Analyse", variant="primary")
+        clear_btn = gr.Button("🧹 Clear", variant="stop")
 
-    def process_with_loading(image):
-        loading.visible = True
-        result = predict_image(image)
-        loading.visible = False
-        return result
+    output_text = gr.Textbox(label="Detection Result", elem_id="result-box")
 
+    # Actions
     analyze_btn.click(
-        predict_image,
-        inputs=[image_input],
-        outputs=[output_text],
-        show_progress=True
+        fn=predict_image,
+        inputs=image_input,
+        outputs=output_text,
+        show_progress=True  # shows built-in Gradio loading spinner
     )
 
     clear_btn.click(lambda: (None, ""), None, [image_input, output_text])