Create app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+import torch
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+from PIL import Image
+
+from ensemble_transformers import EnsembleModelForImageClassification
+ensemble = EnsembleModelForImageClassification.from_multiple_pretrained(
+    "tcvrishank/histo_train_vit", "tcvrishank/histo_train_segformer", "tcvrishank/histo_train_swin"
+)
+
+candidate_labels = ["Benign", "InSitu", "Invasive", "Normal"]
+
+def return_prediction(image):
+  
+    with torch.no_grad():
+        outputs = ensemble(image, mean_pool = True)
+
+    logits = outputs.logits[0]
+    probs = logits.softmax(dim=-1).numpy()
+    scores = probs.tolist()
+
+    result = [
+        {"score": score, "label": candidate_label}
+        for score, candidate_label in sorted(zip(probs, candidate_labels), key=lambda x: -x[0])
+    ]
+    result = result[0]
+    final = f"This histopathology image shows cells that are {round(result['score'] * 100, 2)}% certain to be {result['label']}."
+    return final
+
+
+demo = gr.Interface(fn=return_prediction, inputs="image", outputs="text")
+    
+demo.launch(share = True)