add app and requirements

app.py

@@ -0,0 +1,153 @@
+import gradio as gr
+import torch
+from PIL import Image
+from datasets import load_dataset
+import random
+
+from skincancer_vit.model import SkinCancerViTModel
+
+HF_MODEL_REPO = "ethicalabs/SkinCancerViT"
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+print(f"Loading SkinCancerViT model from {HF_MODEL_REPO} to {DEVICE}...")
+
+model = SkinCancerViTModel.from_pretrained(HF_MODEL_REPO)
+model.to(DEVICE)
+model.eval()  # Set to evaluation mode
+print("Model loaded successfully.")
+
+print("Loading 'marmal88/skin_cancer' dataset for random samples...")
+dataset = load_dataset("marmal88/skin_cancer", split="test")
+print("Dataset loaded successfully.")
+
+
+def predict_uploaded_image(image: Image.Image, age: int, localization: str) -> str:
+    """
+    Handles prediction for an uploaded image with user-provided tabular data.
+    """
+    if model is None:
+        return "Error: Model not loaded. Please check the console for details."
+    if image is None:
+        return "Please upload an image."
+    if age is None:
+        return "Please enter an age."
+    if not localization:
+        return "Please select a localization."
+
+    try:
+        # Call the model's full_predict method
+        predicted_dx, confidence = model.full_predict(
+            raw_image=image, raw_age=age, raw_localization=localization, device=DEVICE
+        )
+        return f"Predicted Diagnosis: **{predicted_dx}** (Confidence: {confidence:.4f})"
+    except Exception as e:
+        return f"Prediction Error: {e}"
+
+
+# --- Prediction Function for Random Sample ---
+def predict_random_sample() -> str:
+    """
+    Fetches a random sample from the dataset and performs prediction.
+    """
+    if model is None:
+        return "Error: Model not loaded. Please check the console for details."
+    if dataset is None:
+        return "Error: Dataset not loaded. Cannot select random sample."
+
+    try:
+        # Select a random sample from the dataset
+        random_idx = random.randint(0, len(dataset) - 1)
+        sample = dataset[random_idx]
+
+        sample_image = sample["image"]
+        sample_age = sample["age"]
+        sample_localization = sample["localization"]
+        sample_true_dx = sample["dx"]
+
+        # Call the model's full_predict method
+        predicted_dx, confidence = model.full_predict(
+            raw_image=sample_image,
+            raw_age=sample_age,
+            raw_localization=sample_localization,
+            device=DEVICE,
+        )
+
+        # Return a formatted string with all information
+        result_str = (
+            f"**Random Sample Details:**\n"
+            f"- Age: {sample_age}\n"
+            f"- Localization: {sample_localization}\n"
+            f"- True Diagnosis: **{sample_true_dx}**\n\n"
+            f"**Model Prediction:**\n"
+            f"- Predicted Diagnosis: **{predicted_dx}**\n"
+            f"- Confidence: {confidence:.4f}\n"
+            f"- Correct Prediction: {'✅ Yes' if predicted_dx == sample_true_dx else '❌ No'}"
+        )
+        return sample_image, result_str
+    except Exception as e:
+        return None, f"Prediction Error on Random Sample: {e}"
+
+
+# --- Gradio Interface ---
+with gr.Blocks(title="Skin Cancer ViT Predictor") as demo:
+    gr.Markdown(
+        """
+        # Skin Cancer ViT Predictor
+        This application demonstrates the `SkinCancerViT` multimodal model for skin cancer diagnosis.
+        It can take an uploaded image with patient metadata or predict on a random sample from the dataset.
+        **Disclaimer:** This tool is for demonstration and research purposes only and should not be used for medical diagnosis.
+        """
+    )
+
+    with gr.Tab("Predict on Random Sample"):
+        gr.Markdown("## Get a Prediction from a Random Sample in the Test Set")
+        random_sample_button = gr.Button("Get Random Sample Prediction")
+
+        # Modified output components for random sample tab
+        with gr.Row():
+            output_random_image = gr.Image(
+                type="pil", label="Random Sample Image", height=250, width=250
+            )
+            output_random_details = gr.Markdown(
+                "Random sample details and prediction will appear here."
+            )
+
+        random_sample_button.click(
+            fn=predict_random_sample,
+            inputs=[],
+            outputs=[
+                output_random_image,
+                output_random_details,
+            ],  # Map to both image and markdown outputs
+        )
+
+    with gr.Tab("Upload Image & Predict"):
+        gr.Markdown("## Upload Your Image and Provide Patient Data")
+        with gr.Row():
+            image_input = gr.Image(
+                type="pil", label="Upload Skin Lesion Image (224x224 preferred)"
+            )
+            with gr.Column():
+                age_input = gr.Number(
+                    label="Patient Age", minimum=0, maximum=120, step=1
+                )
+                # Ensure these localizations match your training data categories
+                localization_input = gr.Dropdown(
+                    model.config.localization_to_id.keys(),
+                    label="Lesion Localization",
+                    value="unknown",  # Default value
+                )
+                predict_button = gr.Button("Get Prediction")
+
+        output_upload = gr.Markdown("Prediction will appear here.")
+
+        predict_button.click(
+            fn=predict_uploaded_image,
+            inputs=[image_input, age_input, localization_input],
+            outputs=output_upload,
+        )
+
+if __name__ == "__main__":
+    demo.launch(share=False)
+

requirements.txt

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+skincancervit @ git+https://github.com/ethicalabs-ai/SkinCancerViT.git