app.py v2

app.py

@@ -1,180 +1,180 @@
-import gradio as gr
-import torch
-from transformers import AutoModel, AutoProcessor
-
-from gender_classification import gender_classification
-from emotion_classification import emotion_classification
-from dog_breed import dog_breed_classification
-from deepfake_quality import deepfake_classification
-from gym_workout_classification import workout_classification
-from augmented_waste_classifier import waste_classification
-from age_classification import age_classification
-from mnist_digits import classify_digit
-from fashion_mnist_cloth import fashion_mnist_classification
-from indian_western_food_classify import food_classification
-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
-from multisource_121 import multisource_classification
-from painting_126 import painting_classification
-from sketch_126 import sketch_classification  # New import
-
-# Main classification function for multi-model classification.
-def classify(image, model_name):
-    if model_name == "gender":
-        return gender_classification(image)
-    elif model_name == "emotion":
-        return emotion_classification(image)
-    elif model_name == "dog breed":
-        return dog_breed_classification(image)
-    elif model_name == "deepfake":
-        return deepfake_classification(image)
-    elif model_name == "gym workout":
-        return workout_classification(image)
-    elif model_name == "waste":
-        return waste_classification(image)
-    elif model_name == "age":
-        return age_classification(image)
-    elif model_name == "mnist":
-        return classify_digit(image)
-    elif model_name == "fashion_mnist":
-        return fashion_mnist_classification(image)
-    elif model_name == "food":
-        return food_classification(image)
-    elif model_name == "bird":
-        return bird_classification(image)
-    elif model_name == "leaf disease":
-        return classify_leaf_disease(image)
-    elif model_name == "sign language":
-        return sign_language_classification(image)
-    elif model_name == "traffic density":
-        return traffic_density_classification(image)
-    elif model_name == "clip art":
-        return clipart_classification(image)
-    elif model_name == "multisource":
-        return multisource_classification(image)
-    elif model_name == "painting":
-        return painting_classification(image)
-    elif model_name == "sketch":  # New option
-        return sketch_classification(image)
-    else:
-        return {"Error": "No model selected"}
-
-# Function to update the selected model and button styles.
-def select_model(model_name):
-    model_variants = {
-        "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",
-        "multisource": "secondary", "painting": "secondary", "sketch": "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)
-sg1_ckpt = "google/siglip-so400m-patch14-384"
-siglip1_model = AutoModel.from_pretrained(sg1_ckpt, device_map="cpu").eval()
-siglip1_processor = AutoProcessor.from_pretrained(sg1_ckpt)
-
-sg2_ckpt = "google/siglip2-so400m-patch14-384"
-siglip2_model = AutoModel.from_pretrained(sg2_ckpt, device_map="cpu").eval()
-siglip2_processor = AutoProcessor.from_pretrained(sg2_ckpt)
-
-def postprocess_siglip(sg1_probs, sg2_probs, labels):
-    sg1_output = {labels[i]: sg1_probs[0][i].item() for i in range(len(labels))}
-    sg2_output = {labels[i]: sg2_probs[0][i].item() for i in range(len(labels))}
-    return sg1_output, sg2_output
-
-def siglip_detector(image, texts):
-    sg1_inputs = siglip1_processor(
-        text=texts, images=image, return_tensors="pt", padding="max_length", max_length=64
-    ).to("cpu")
-    sg2_inputs = siglip2_processor(
-        text=texts, images=image, return_tensors="pt", padding="max_length", max_length=64
-    ).to("cpu")
-    with torch.no_grad():
-        sg1_outputs = siglip1_model(**sg1_inputs)
-        sg2_outputs = siglip2_model(**sg2_inputs)
-        sg1_logits_per_image = sg1_outputs.logits_per_image
-        sg2_logits_per_image = sg2_outputs.logits_per_image
-        sg1_probs = torch.sigmoid(sg1_logits_per_image)
-        sg2_probs = torch.sigmoid(sg2_logits_per_image)
-    return sg1_probs, sg2_probs
-
-def infer(image, candidate_labels):
-    candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",")]
-    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 tabs.
-with gr.Blocks(theme="YTheme/Minecraft") as demo:
-    gr.Markdown("# Multi-Domain & Zero-Shot Image Classification")
-    
-    with gr.Tabs():
-        # Tab 1: Multi-Model Classification
-        with gr.Tab("Multi-Domain Classification"):
-            with gr.Sidebar():
-                gr.Markdown("# Choose Domain")
-                with gr.Row():
-                    age_btn = gr.Button("Age Classification", variant="primary")
-                    gender_btn = gr.Button("Gender Classification", variant="secondary")
-                    emotion_btn = gr.Button("Emotion Classification", variant="secondary")
-                    gym_workout_btn = gr.Button("Gym Workout Classification", variant="secondary")
-                    dog_breed_btn = gr.Button("Dog Breed Classification", variant="secondary")
-                    bird_btn = gr.Button("Bird Species Classification", variant="secondary")
-                    waste_btn = gr.Button("Waste Classification", variant="secondary")
-                    deepfake_btn = gr.Button("Deepfake Quality Test", variant="secondary")
-                    traffic_density_btn = gr.Button("Traffic Density", variant="secondary")
-                    sign_language_btn = gr.Button("Alphabet Sign Language", variant="secondary")
-                    clip_art_btn = gr.Button("Clip Art 126", variant="secondary")
-                    mnist_btn = gr.Button("Digit Classify (0-9)", variant="secondary")
-                    fashion_mnist_btn = gr.Button("Fashion MNIST (only cloth)", variant="secondary")
-                    food_btn = gr.Button("Indian/Western Food Type", variant="secondary")
-                    leaf_disease_btn = gr.Button("Rice Leaf Disease", variant="secondary")
-                    multisource_btn = gr.Button("Multi Source 121", variant="secondary")
-                    painting_btn = gr.Button("Painting 126", variant="secondary")
-                    sketch_btn = gr.Button("Sketch 126", variant="secondary")
-                    
-                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, clip_art_btn, multisource_btn, painting_btn, sketch_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", "multisource", "painting", "sketch"  # 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)
-
-            with gr.Row():
-                with gr.Column():
-                    image_input = gr.Image(type="numpy", label="Upload Image")
-                    analyze_btn = gr.Button("Classify / Predict")
-                output_label = gr.Label(label="Prediction Scores")
-                analyze_btn.click(fn=classify, inputs=[image_input, selected_model], outputs=output_label)
-        
-        # Tab 2: Zero-Shot Classification (SigLIP)
-        with gr.Tab("Zero-Shot Classification"):
-            gr.Markdown("## Compare SigLIP 1 and SigLIP 2 on Zero-Shot Classification")
-            with gr.Row():
-                with gr.Column():
-                    zs_image_input = gr.Image(type="pil", label="Upload Image")
-                    zs_text_input = gr.Textbox(label="Input a list of labels (comma separated)")
-                    zs_run_button = gr.Button("Run")
-                with gr.Column():
-                    siglip1_output = gr.Label(label="SigLIP 1 Output", num_top_classes=3)
-                    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])
-
+import gradio as gr
+import torch
+from transformers import AutoModel, AutoProcessor
+
+from gender_classification import gender_classification
+from emotion_classification import emotion_classification
+from dog_breed import dog_breed_classification
+from deepfake_quality import deepfake_classification
+from gym_workout_classification import workout_classification
+from augmented_waste_classifier import waste_classification
+from age_classification import age_classification
+from mnist_digits import classify_digit
+from fashion_mnist_cloth import fashion_mnist_classification
+from indian_western_food_classify import food_classification
+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
+from multisource_121 import multisource_classification
+from painting_126 import painting_classification
+from sketch_126 import sketch_classification  # New import
+
+# Main classification function for multi-model classification.
+def classify(image, model_name):
+    if model_name == "gender":
+        return gender_classification(image)
+    elif model_name == "emotion":
+        return emotion_classification(image)
+    elif model_name == "dog breed":
+        return dog_breed_classification(image)
+    elif model_name == "deepfake":
+        return deepfake_classification(image)
+    elif model_name == "gym workout":
+        return workout_classification(image)
+    elif model_name == "waste":
+        return waste_classification(image)
+    elif model_name == "age":
+        return age_classification(image)
+    elif model_name == "mnist":
+        return classify_digit(image)
+    elif model_name == "fashion_mnist":
+        return fashion_mnist_classification(image)
+    elif model_name == "food":
+        return food_classification(image)
+    elif model_name == "bird":
+        return bird_classification(image)
+    elif model_name == "leaf disease":
+        return classify_leaf_disease(image)
+    elif model_name == "sign language":
+        return sign_language_classification(image)
+    elif model_name == "traffic density":
+        return traffic_density_classification(image)
+    elif model_name == "clip art":
+        return clipart_classification(image)
+    elif model_name == "multisource":
+        return multisource_classification(image)
+    elif model_name == "painting":
+        return painting_classification(image)
+    elif model_name == "sketch":  # New option
+        return sketch_classification(image)
+    else:
+        return {"Error": "No model selected"}
+
+# Function to update the selected model and button styles.
+def select_model(model_name):
+    model_variants = {
+        "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",
+        "multisource": "secondary", "painting": "secondary", "sketch": "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)
+sg1_ckpt = "google/siglip-so400m-patch14-384"
+siglip1_model = AutoModel.from_pretrained(sg1_ckpt, device_map="cpu").eval()
+siglip1_processor = AutoProcessor.from_pretrained(sg1_ckpt)
+
+sg2_ckpt = "google/siglip2-so400m-patch14-384"
+siglip2_model = AutoModel.from_pretrained(sg2_ckpt, device_map="cpu").eval()
+siglip2_processor = AutoProcessor.from_pretrained(sg2_ckpt)
+
+def postprocess_siglip(sg1_probs, sg2_probs, labels):
+    sg1_output = {labels[i]: sg1_probs[0][i].item() for i in range(len(labels))}
+    sg2_output = {labels[i]: sg2_probs[0][i].item() for i in range(len(labels))}
+    return sg1_output, sg2_output
+
+def siglip_detector(image, texts):
+    sg1_inputs = siglip1_processor(
+        text=texts, images=image, return_tensors="pt", padding="max_length", max_length=64
+    ).to("cpu")
+    sg2_inputs = siglip2_processor(
+        text=texts, images=image, return_tensors="pt", padding="max_length", max_length=64
+    ).to("cpu")
+    with torch.no_grad():
+        sg1_outputs = siglip1_model(**sg1_inputs)
+        sg2_outputs = siglip2_model(**sg2_inputs)
+        sg1_logits_per_image = sg1_outputs.logits_per_image
+        sg2_logits_per_image = sg2_outputs.logits_per_image
+        sg1_probs = torch.sigmoid(sg1_logits_per_image)
+        sg2_probs = torch.sigmoid(sg2_logits_per_image)
+    return sg1_probs, sg2_probs
+
+def infer(image, candidate_labels):
+    candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",")]
+    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 tabs.
+with gr.Blocks() as demo:
+    gr.Markdown("# Multi-Domain & Zero-Shot Image Classification")
+    
+    with gr.Tabs():
+        # Tab 1: Multi-Model Classification
+        with gr.Tab("Multi-Domain Classification"):
+            with gr.Sidebar():
+                gr.Markdown("# Choose Domain")
+                with gr.Row():
+                    age_btn = gr.Button("Age Classification", variant="primary")
+                    gender_btn = gr.Button("Gender Classification", variant="secondary")
+                    emotion_btn = gr.Button("Emotion Classification", variant="secondary")
+                    gym_workout_btn = gr.Button("Gym Workout Classification", variant="secondary")
+                    dog_breed_btn = gr.Button("Dog Breed Classification", variant="secondary")
+                    bird_btn = gr.Button("Bird Species Classification", variant="secondary")
+                    waste_btn = gr.Button("Waste Classification", variant="secondary")
+                    deepfake_btn = gr.Button("Deepfake Quality Test", variant="secondary")
+                    traffic_density_btn = gr.Button("Traffic Density", variant="secondary")
+                    sign_language_btn = gr.Button("Alphabet Sign Language", variant="secondary")
+                    clip_art_btn = gr.Button("Clip Art 126", variant="secondary")
+                    mnist_btn = gr.Button("Digit Classify (0-9)", variant="secondary")
+                    fashion_mnist_btn = gr.Button("Fashion MNIST (only cloth)", variant="secondary")
+                    food_btn = gr.Button("Indian/Western Food Type", variant="secondary")
+                    leaf_disease_btn = gr.Button("Rice Leaf Disease", variant="secondary")
+                    multisource_btn = gr.Button("Multi Source 121", variant="secondary")
+                    painting_btn = gr.Button("Painting 126", variant="secondary")
+                    sketch_btn = gr.Button("Sketch 126", variant="secondary")
+                    
+                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, clip_art_btn, multisource_btn, painting_btn, sketch_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", "multisource", "painting", "sketch"  # 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)
+
+            with gr.Row():
+                with gr.Column():
+                    image_input = gr.Image(type="numpy", label="Upload Image")
+                    analyze_btn = gr.Button("Classify / Predict")
+                output_label = gr.Label(label="Prediction Scores")
+                analyze_btn.click(fn=classify, inputs=[image_input, selected_model], outputs=output_label)
+        
+        # Tab 2: Zero-Shot Classification (SigLIP)
+        with gr.Tab("Zero-Shot Classification"):
+            gr.Markdown("## Compare SigLIP 1 and SigLIP 2 on Zero-Shot Classification")
+            with gr.Row():
+                with gr.Column():
+                    zs_image_input = gr.Image(type="pil", label="Upload Image")
+                    zs_text_input = gr.Textbox(label="Input a list of labels (comma separated)")
+                    zs_run_button = gr.Button("Run")
+                with gr.Column():
+                    siglip1_output = gr.Label(label="SigLIP 1 Output", num_top_classes=3)
+                    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()