Create app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load the model and processor
+processor = AutoImageProcessor.from_pretrained("prithivMLmods/Fire-Detection-Engine")
+model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Fire-Detection-Engine")
+
+def predict(image):
+    # Convert image to expected format
+    image = Image.fromarray(image)
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax(-1).item()
+    
+    return f"Predicted class: {predicted_class}"
+
+# Create Gradio app
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Textbox(),
+    title="Fire Detection Engine",
+    description="Upload an image to check for fire."
+)
+
+iface.launch()