Update app.py

app.py

@@ -5,7 +5,7 @@ from PIL import Image
 import io
 import sys
 import gradio as gr
-from tensorflow.keras.applications.densenet import preprocess_input
+from tensorflow.keras.applications.resnet50 import preprocess_input
 
 # =========================
 # 1. FastAPI Init
@@ -17,7 +17,7 @@ app = FastAPI(title="Ashoka Buried Penis Classifier API")
 # =========================
 print("Loading model...")
 try:
-    model = tf.keras.models.load_model("cnn_kfold_best_model_v2.h5")
+    model = tf.keras.models.load_model("cnn_kfold_best_model.h5")
     print("Model loaded successfully")
 except Exception as e:
     print("Failed to load model:", e)
@@ -56,6 +56,8 @@ def predict_image(image):
 # =========================
 # 5. FastAPI Endpoint
 # =========================
+
+
 @app.post("/predict")
 async def api_predict(file: UploadFile = File(...)):
     image_bytes = await file.read()
@@ -75,7 +77,7 @@ async def api_predict(file: UploadFile = File(...)):
 # =========================
 with gr.Blocks() as demo:
     gr.Markdown("""
-    # Ashoka Hipospadia Classifier API - DenseNet
+    # Ashoka Hipospadia Classifier API - ResNet50
     **Medical screening tool for Buried Penis**
 
     ⚠️ This tool is **NOT a diagnostic device**.  
@@ -103,4 +105,4 @@ with gr.Blocks() as demo:
 # =========================
 # 7. Mount Gradio to FastAPI
 # =========================
-app = gr.mount_gradio_app(app, demo, path="/")
+app = gr.mount_gradio_app(app, demo, path="/")