Update app.py

app.py

@@ -1,19 +1,18 @@
-# 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
+import os
 
-# Download model from Hugging Face Hub
+# Disable GPU usage
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+# Download and load model
 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
+# Inference function
 def predict_tb(img: Image.Image):
     try:
         image = img.convert("RGB").resize((224, 224))
@@ -26,3 +25,14 @@ def predict_tb(img: Image.Image):
     except Exception as e:
         return f"❌ Error during prediction: {str(e)}"
 
+# Gradio UI
+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."
+)
+
+# Launch the app
+iface.launch()