Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load a free Hugging Face model (small + free to run)
+generator = pipeline("text2text-generation", model="google/flan-t5-small")
+
+# Agent function
+def agentic_ai(user_input):
+    # Step 1: Analyze input
+    analysis_prompt = f"Analyze the intent of this input: {user_input}"
+    analysis = generator(analysis_prompt, max_length=50, do_sample=False)[0]['generated_text']
+
+    # Step 2: Decide what to do (simple rule-based agent)
+    if "summarize" in user_input.lower():
+        task_prompt = f"Summarize this text in 2 lines: {user_input}"
+    elif "question" in user_input.lower() or "?" in user_input:
+        task_prompt = f"Answer this question briefly: {user_input}"
+    else:
+        task_prompt = f"Generate a helpful response: {user_input}"
+
+    # Step 3: LLM Response
+    response = generator(task_prompt, max_length=80, do_sample=False)[0]['generated_text']
+
+    # Step 4: Return both analysis + final response
+    return f"🔎 Agent Analysis: {analysis}\n\n💡 Agent Response: {response}"
+
+
+# Gradio UI
+demo = gr.Interface(
+    fn=agentic_ai,
+    inputs=gr.Textbox(lines=3, placeholder="Type your text here..."),
+    outputs="text",
+    title="🤖 Mini Agentic LLM App",
+    description="Smallest free demo of an Agentic AI using NLP + LLM on Hugging Face & Gradio."
+)
+
+if __name__ == "__main__":
+    demo.launch()