Update app/main.py

app/main.py

@@ -1,65 +1,60 @@
-# ---------------------------
-# File: app/main.py
-# ---------------------------
-from fastapi import FastAPI, UploadFile, File, Query
-from fastapi.responses import JSONResponse, StreamingResponse
-from PIL import Image
-import io
-import numpy as np
-import traceback
-
-# Import the model utilities
-from app.model import predict, gradcam, CLASS_NAMES
-
-app = FastAPI(title="Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)")
-
-@app.post("/predict")
-async def predict_image(file: UploadFile = File(...)):
-    try:
-        contents = await file.read()
-        pil_img = Image.open(io.BytesIO(contents)).convert("RGB")
-        label, confidence, probs = predict(pil_img)
-        return JSONResponse({
-            "predicted_label": label,
-            "confidence": round(confidence, 3),
-            "probabilities": {k: round(v, 6) for k, v in probs.items()}
-        })
-    except Exception as e:
-        tb = traceback.format_exc()
-        return JSONResponse({"error": str(e), "trace": tb}, status_code=500)
-
-@app.post("/gradcam")
-async def gradcam_image(file: UploadFile = File(...), interpolant: float = Query(0.5, ge=0.0, le=1.0)):
-    """
-    Returns a PNG image (overlay) produced by gradcam().
-    `interpolant` controls mixing (0..1).
-    """
-    try:
-        contents = await file.read()
-        pil_img = Image.open(io.BytesIO(contents)).convert("RGB")
-
-        # Compute overlay (this calls the optimized gradcam in model.py)
-        overlay = gradcam(pil_img, interpolant=float(interpolant))
-
-        # Ensure correct dtype and shape
-        overlay = np.asarray(overlay).astype("uint8")
-        if overlay.ndim == 2:
-            overlay = np.stack([overlay] * 3, axis=-1)
-
-        # Convert to PNG bytes
-        buf = io.BytesIO()
-        Image.fromarray(overlay).save(buf, format="PNG")
-        buf.seek(0)
-        return StreamingResponse(buf, media_type="image/png")
-
-    except Exception as e:
-        tb = traceback.format_exc()
-        return JSONResponse({"error": str(e), "trace": tb}, status_code=500)
-
-# Optional health endpoint
-@app.get("/health")
-async def health():
-    return {"status": "ok", "classes": CLASS_NAMES}
-# ---------------------------
-# End of app/main.py
-# ---------------------------
+# File: app/main.py
+from fastapi import FastAPI, UploadFile, File, Query
+from fastapi.responses import JSONResponse, StreamingResponse
+from PIL import Image
+import io
+import numpy as np
+import traceback
+
+# Import the model utilities
+from app.model import predict, gradcam, CLASS_NAMES
+
+app = FastAPI(title="Brain Tumor MRI Classifier (InceptionV3 + Grad-CAM)")
+
+@app.post("/predict")
+async def predict_image(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        pil_img = Image.open(io.BytesIO(contents)).convert("RGB")
+        label, confidence, probs = predict(pil_img)
+        return JSONResponse({
+            "predicted_label": label,
+            "confidence": round(confidence, 3),
+            "probabilities": {k: round(v, 6) for k, v in probs.items()}
+        })
+    except Exception as e:
+        tb = traceback.format_exc()
+        return JSONResponse({"error": str(e), "trace": tb}, status_code=500)
+
+@app.post("/gradcam")
+async def gradcam_image(file: UploadFile = File(...), interpolant: float = Query(0.5, ge=0.0, le=1.0)):
+    """
+    Returns a PNG image (overlay) produced by gradcam().
+    `interpolant` controls mixing (0..1).
+    """
+    try:
+        contents = await file.read()
+        pil_img = Image.open(io.BytesIO(contents)).convert("RGB")
+
+        # Compute overlay (this calls the optimized gradcam in model.py)
+        overlay = gradcam(pil_img, interpolant=float(interpolant))
+
+        # Ensure correct dtype and shape
+        overlay = np.asarray(overlay).astype("uint8")
+        if overlay.ndim == 2:
+            overlay = np.stack([overlay] * 3, axis=-1)
+
+        # Convert to PNG bytes
+        buf = io.BytesIO()
+        Image.fromarray(overlay).save(buf, format="PNG")
+        buf.seek(0)
+        return StreamingResponse(buf, media_type="image/png")
+
+    except Exception as e:
+        tb = traceback.format_exc()
+        return JSONResponse({"error": str(e), "trace": tb}, status_code=500)
+
+# Optional health endpoint
+@app.get("/health")
+async def health():
+    return {"status": "ok", "classes": CLASS_NAMES}