Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from datasets import load_dataset
+
+# Load model from Hugging Face Hub
+model_name = "Qwen/Qwen1.5-7B-Chat"  # or any other LLM
+pipe = pipeline("text-generation", model=model_name)
+
+# Optionally load GTA queries
+gta_data = load_dataset("open-compass/GTA", split="test")
+
+def run_model(user_query, use_sample):
+    if use_sample:
+        question = gta_data[int(user_query)]["question"]
+    else:
+        question = user_query
+
+    output = pipe(question, max_new_tokens=256, do_sample=True)
+    return f"**Input**: {question}\n\n**Output**:\n{output[0]['generated_text']}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 GTA Tool Reasoning Demo with Hugging Face Models")
+    with gr.Row():
+        user_input = gr.Textbox(label="Enter your query or GTA index (if using sample)")
+        use_sample = gr.Checkbox(label="Use sample from GTA dataset (enter index)", value=False)
+    run_button = gr.Button("Run")
+    output = gr.Markdown()
+    
+    run_button.click(run_model, inputs=[user_input, use_sample], outputs=output)
+
+demo.launch()