Update app.py

app.py

@@ -12,18 +12,18 @@ processor = AutoImageProcessor.from_pretrained(model_name)
 model = AutoModelForImageClassification.from_pretrained(model_name)
 label_map = model.config.id2label
 
-# Skin condition descriptions (simple language)
+# Match labels exactly from model
 condition_info = {
-    "actinic keratoses": "Dry, rough patch – sometimes early sign of skin cancer.",
-    "basal cell carcinoma": "Slow-growing skin cancer. Common but treatable.",
-    "benign keratosis-like lesions": "Non-cancerous growth. Like age spots or warts.",
+    "actinic_keratoses": "Dry, rough patch – sometimes early sign of skin cancer.",
+    "basal_cell_carcinoma": "Slow-growing skin cancer. Common but treatable.",
+    "benign_keratosis-like_lesions": "Non-cancerous growth. Like age spots or warts.",
     "dermatofibroma": "Small, firm bump. Usually harmless.",
-    "melanocytic nevi": "Just a mole. Normal unless changing.",
+    "melanocytic_nevi": "Just a mole. Normal unless changing.",
     "melanoma": "Dangerous skin cancer. Needs fast treatment.",
-    "vascular lesions": "Red or purple patches from blood vessels."
+    "vascular_lesions": "Red or purple patches from blood vessels."
 }
 
-# AI logic
+# AI prediction logic
 def classify_skin(image: Image.Image):
     if image is None:
         return pd.DataFrame(), "Please upload or take a photo."
@@ -41,17 +41,18 @@ def classify_skin(image: Image.Image):
     likely_conditions = []
 
     for idx, prob in enumerate(probs):
-        label = label_map[idx]
+        label = label_map[idx]  # this is the exact model label
         conf = prob.item()
         status = "✅ Positive" if conf > threshold else "❌ Negative"
+        desc = condition_info.get(label, "No description available.")
         data.append({
-            "Condition": label.capitalize(),
+            "Condition": label.replace("_", " ").capitalize(),
             "Confidence (%)": f"{conf*100:.2f}",
             "Status": status,
-            "What it means": condition_info[label.lower()]
+            "What it means": desc
         })
         if conf > threshold:
-            likely_conditions.append(label.capitalize())
+            likely_conditions.append(label.replace("_", " ").capitalize())
 
     df = pd.DataFrame(data)
     summary_text = (
@@ -62,7 +63,7 @@ def classify_skin(image: Image.Image):
 
     return df, summary_text
 
-# Gradio Interface
+# Gradio UI
 demo = gr.Interface(
     fn=classify_skin,
     inputs=gr.Image(type="pil", label="📷 Upload or Capture Skin Image"),
@@ -71,7 +72,7 @@ demo = gr.Interface(
         gr.Markdown()
     ],
     title="AI Skin Condition Classifier",
-    description="Upload a photo of a skin issue. The AI will check 7 common conditions and suggest what looks likely. For support only — not a replacement for a real doctor."
+    description="Upload a photo of a skin issue. The AI will check 7 common conditions and suggest what's likely. For support only — not a diagnosis."
 )
 
 if __name__ == "__main__":