Update app.py

app.py

@@ -17,6 +17,7 @@ from bird_species import bird_classification
 from alphabet_sign_language_detection import sign_language_classification
 from rice_leaf_disease import classify_leaf_disease
 from traffic_density import traffic_density_classification
+from clip_art import clipart_classification  # New import
 
 # Main classification function for multi-model classification.
 def classify(image, model_name):
@@ -48,6 +49,8 @@ def classify(image, model_name):
         return sign_language_classification(image)
     elif model_name == "traffic density":
         return traffic_density_classification(image)
+    elif model_name == "clip art":  # New option
+        return clipart_classification(image)
     else:
         return {"Error": "No model selected"}
 
@@ -57,7 +60,7 @@ def select_model(model_name):
         "gender": "secondary", "emotion": "secondary", "dog breed": "secondary", "deepfake": "secondary",
         "gym workout": "secondary", "waste": "secondary", "age": "secondary", "mnist": "secondary",
         "fashion_mnist": "secondary", "food": "secondary", "bird": "secondary", "leaf disease": "secondary",
-        "sign language": "secondary", "traffic density": "secondary"
+        "sign language": "secondary", "traffic density": "secondary", "clip art": "secondary"  # New model variant
     }
     model_variants[model_name] = "primary"
     return (model_name, *(gr.update(variant=model_variants[key]) for key in model_variants))
@@ -101,9 +104,9 @@ def infer(image, candidate_labels):
     sg1_probs, sg2_probs = siglip_detector(image, candidate_labels)
     return postprocess_siglip(sg1_probs, sg2_probs, labels=candidate_labels)
 
-# Build the Gradio Interface with two tab
+# Build the Gradio Interface with two tabs.
 with gr.Blocks() as demo:
-    gr.Markdown("# Multi-Model & Zero-Shot Classification Interface")
+    gr.Markdown("# Multi-Domain & Zero-Shot Image Classification")
     
     with gr.Tabs():
         # Tab 1: Multi-Model Classification
@@ -125,14 +128,23 @@ with gr.Blocks() as demo:
                     leaf_disease_btn = gr.Button("Rice Leaf Disease", variant="secondary")
                     sign_language_btn = gr.Button("Alphabet Sign Language", variant="secondary")
                     traffic_density_btn = gr.Button("Traffic Density", variant="secondary")
+                    clip_art_btn = gr.Button("Art Classification", variant="secondary")  # New button
                 
                 selected_model = gr.State("age")
                 gr.Markdown("### Current Model:")
                 model_display = gr.Textbox(value="age", interactive=False)
                 selected_model.change(lambda m: m, selected_model, model_display)
 
-                buttons = [gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn, age_btn, mnist_btn, fashion_mnist_btn, food_btn, bird_btn, leaf_disease_btn, sign_language_btn, traffic_density_btn]
-                model_names = ["gender", "emotion", "dog breed", "deepfake", "gym workout", "waste", "age", "mnist", "fashion_mnist", "food", "bird", "leaf disease", "sign language", "traffic density"]
+                buttons = [
+                    gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn,
+                    age_btn, mnist_btn, fashion_mnist_btn, food_btn, bird_btn, leaf_disease_btn,
+                    sign_language_btn, traffic_density_btn, clip_art_btn  # Include new button
+                ]
+                model_names = [
+                    "gender", "emotion", "dog breed", "deepfake", "gym workout", "waste",
+                    "age", "mnist", "fashion_mnist", "food", "bird", "leaf disease",
+                    "sign language", "traffic density", "clip art"  # New model name
+                ]
 
                 for btn, name in zip(buttons, model_names):
                     btn.click(fn=lambda n=name: select_model(n), inputs=[], outputs=[selected_model] + buttons)