Update app.py

app.py

@@ -22,9 +22,9 @@ 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
+from multisource_121 import multisource_classification  # New import
 
-#Gradio-Theme
-
+# Gradio-Theme
 class Seafoam(Base):
     def __init__(
         self,
@@ -93,8 +93,10 @@ 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
+    elif model_name == "clip art":
         return clipart_classification(image)
+    elif model_name == "multisource":
+        return multisource_classification(image)
     else:
         return {"Error": "No model selected"}
 
@@ -104,13 +106,13 @@ 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", "clip art": "secondary"  # New model variant
+        "sign language": "secondary", "traffic density": "secondary", "clip art": "secondary",
+        "multisource": "secondary"  # New model variant
     }
     model_variants[model_name] = "primary"
     return (model_name, *(gr.update(variant=model_variants[key]) for key in model_variants))
 
 # Zero-Shot Classification Setup (SigLIP models)
-
 # Load the SigLIP models and processors
 sg1_ckpt = "google/siglip-so400m-patch14-384"
 siglip1_model = AutoModel.from_pretrained(sg1_ckpt, device_map="cpu").eval()
@@ -172,7 +174,8 @@ with gr.Blocks(theme=seafoam) 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
+                    clip_art_btn = gr.Button("Art Classification", variant="secondary")
+                    multisource_btn = gr.Button("Multi-Source Classification", variant="secondary")  # New button
                 
                 selected_model = gr.State("age")
                 gr.Markdown("### Current Model:")
@@ -182,12 +185,12 @@ with gr.Blocks(theme=seafoam) as demo:
                 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
+                    sign_language_btn, traffic_density_btn, clip_art_btn, multisource_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
+                    "sign language", "traffic density", "clip art", "multisource"  # New model name
                 ]
 
                 for btn, name in zip(buttons, model_names):
@@ -213,4 +216,4 @@ with gr.Blocks(theme=seafoam) as demo:
                     siglip2_output = gr.Label(label="SigLIP 2 Output", num_top_classes=3)
             zs_run_button.click(fn=infer, inputs=[zs_image_input, zs_text_input], outputs=[siglip1_output, siglip2_output])
 
-demo.launch()
+demo.launch()