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app.py7.txt

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+from transformers import AutoModelForImageClassification, AutoImageProcessor
+import torch
+import torch.nn.functional as F
+from PIL import Image
+import gradio as gr
+
+# -----------------------------
+# 1. Load the pretrained model
+# -----------------------------
+model_name = "microsoft/resnet-50"  # fine-tuned for chest x-ray multi-disease
+model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+model.eval()
+
+# Example disease list (adjust depending on model config)
+diseases = ["Pneumonia", "Effusion", "Atelectasis"]
+
+# -----------------------------
+# 2. Prediction function
+# -----------------------------
+def predict(image):
+    img = image.convert("RGB").resize((224, 224))
+    
+    inputs = processor(images=img, return_tensors="pt")
+    
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    
+    probs = F.softmax(logits, dim=1).squeeze()
+    
+    # Get top-3 predictions
+    top_probs, top_idxs = torch.topk(probs, k=3)
+    
+    results = []
+    for idx, prob in zip(top_idxs, top_probs):
+        disease_name = diseases[idx] if idx < len(diseases) else f"Class {idx.item()}"
+        results.append(f"{disease_name}: {prob.item():.2f}")
+    
+    return "\n".join(results)
+
+# -----------------------------
+# 3. Gradio interface
+# -----------------------------
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Chest X-ray Detector",
+    description="Upload a chest X-ray. The model predicts Pneumonia, Effusion, or Atelectasis."
+)
+
+iface.launch()

requirements1.txt

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+torch
+transformers
+gradio
+pillow