Create app.py

app.py

@@ -0,0 +1,26 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+# Load the model and processor from HF Hub
+model_name = "NickoSELI/blip2-indian-food-captioning-private-checkopt-mock1"
+model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=True)
+processor = AutoProcessor.from_pretrained(model_name, use_auth_token=True)
+
+# Define a prediction function
+def predict(image):
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    caption = processor.decode(outputs[0], skip_special_tokens=True)
+    return caption
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs="text",
+    title="Indian Food Captioning Model"
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()