Update app.py

app.py

@@ -2,14 +2,9 @@ import gradio as gr
 import numpy as np
 from PIL import Image
 from tensorflow.keras.models import load_model
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import HTMLResponse
-import threading
-
 
+# Load model
 model = load_model("chest_xray_model.h5")
-threshold = 0.3  
 
 def predict_image(img):
     img = img.convert("RGB")
@@ -21,31 +16,11 @@ def predict_image(img):
     confidences = {"NORMAL": float(1 - proba), "PNEUMONIA": float(proba)}
     return confidences, img
 
-# =====  Gradio =====
-gradio_interface = gr.Interface(
+# Gradio app
+gr.Interface(
     fn=predict_image,
     inputs=gr.Image(type="pil"),
     outputs=[gr.Label(num_top_classes=2), gr.Image(type="pil")],
     title="Chest X-ray Pneumonia Classifier",
-    description="Upload any Chest X-ray image to predict NORMAL or PNEUMONIA."
-)
-
-# ===== FastAPI setup =====
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# 
-@app.get("/", response_class=HTMLResponse)
-def home():
-    return gradio_interface.launch(inline=True, share=False, prevent_thread_lock=True)
-
-# 
-def run_gradio():
-    gradio_interface.launch(server_name="0.0.0.0", server_port=7860, share=False)
-
-threading.Thread(target=run_gradio, daemon=True).start()
+    description="Upload a Chest X-ray image to predict NORMAL or PNEUMONIA."
+).launch()