Create app.py

app.py

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+import gradio as gr
+from model_tools import extract_task, scrape_huggingface_models
+
+# Final agent with Gradio UI
+def run_agent(user_query: str):
+    """
+    Given a user query, extracts the ML task, finds relevant models, and formats results in markdown.
+    This function is used for Gradio UI interaction.
+    """
+    try:
+        # 1. Extract the standard ML task (e.g., "text-classification")
+        task = extract_task(user_query)
+
+        # 2. Get relevant models for the task
+        models = scrape_huggingface_models(task)
+
+        if not models:
+            return f"❌ No models found for task `{task}`. Try refining your query."
+
+        # 3. Format response as a markdown table
+        response = f"### 🔍 Models for task: `{task}`\n\n"
+        response += "| Model Name | Task | Architecture |\n"
+        response += "|------------|------|---------------|\n"
+
+        for model in models:
+            name = model.get("model_name", "unknown")
+            task_name = model.get("task", "unknown")
+            arch = model.get("architecture", "unknown")
+            response += f"| [{name}](https://huggingface.co/{name}) | {task_name} | {arch} |\n"
+
+        return response
+
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Gradio interface for deployment
+def gradio_ui():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Hugging Face Model Finder Agent")
+        gr.Markdown("Enter a task description, and I'll find suitable ML models for you!")
+
+        # User input for task description
+        user_input = gr.Textbox(label="Describe the ML Task", placeholder="e.g., 'I need a text summarization model'", lines=2)
+
+        # Output for model search results
+        output = gr.Markdown()
+
+        # Connect the input/output to the agent
+        user_input.submit(run_agent, inputs=user_input, outputs=output)
+
+    return demo
+
+# Run the Gradio interface (will run locally, and can be deployed to Spaces)
+if __name__ == "__main__":
+    gradio_ui().launch()