Add application file

app.py

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+import gradio as gr
+import torch
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+# Load the model and processor
+model_id = "google/functiongemma-270m-it"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+
+def generate_tool_call(user_prompt):
+    # This model is designed for function calling/tool use
+    # It requires a specific prompt format (see model card for details)
+    inputs = processor(text=user_prompt, return_tensors="pt").to(model.device)
+    output = model.generate(**inputs, max_new_tokens=128)
+    return processor.decode(output[0], skip_special_tokens=True)
+
+demo = gr.Interface(
+    fn=generate_tool_call,
+    inputs="text",
+    outputs="text",
+    title="FunctionGemma 270M Demo",
+    description="A lightweight 270M model specialized in function calling."
+)
+
+demo.launch()