Update app.py

app.py

@@ -1,18 +1,19 @@
-# Install dependencies (Hugging Face handles these in requirements.txt usually)
-from huggingface_hub import login, hf_hub_download
-import tensorflow as tf
+# Disable GPU to avoid CUDA errors on Hugging Face Spaces
 import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+# Install dependencies
+from huggingface_hub import hf_hub_download
+import tensorflow as tf
 import gradio as gr
 import numpy as np
 from PIL import Image
 
-# Hugging Face login (token not required in public Spaces, skip if not private)
-# login(token="your_token_here")  # Optional
-
-# Download model
+# Download model from Hugging Face Hub
 model_path = hf_hub_download(repo_id="Owos/tb-classifier", filename="tb_model.h5")
 model = tf.keras.models.load_model(model_path)
 
+# Define prediction function
 def predict_tb(img: Image.Image):
     try:
         image = img.convert("RGB").resize((224, 224))
@@ -25,12 +26,3 @@ def predict_tb(img: Image.Image):
     except Exception as e:
         return f"❌ Error during prediction: {str(e)}"
 
-iface = gr.Interface(
-    fn=predict_tb,
-    inputs=gr.Image(type="pil", label="Upload Chest X-ray Image"),
-    outputs="text",
-    title="🩻 Tuberculosis Detection from Chest X-ray",
-    description="Upload a chest X-ray to detect signs of Tuberculosis using an AI model (ResNet50). For educational & demo use only."
-)
-
-iface.launch()