GermanySutherland/Agentic-AI-NLP-LLM

1import gradio as gr2from transformers import pipeline3 4# Load a free Hugging Face model (small + free to run)5generator = pipeline("text2text-generation", model="google/flan-t5-small")6 7# Agent function8def agentic_ai(user_input):9    # Step 1: Analyze input10    analysis_prompt = f"Analyze the intent of this input: {user_input}"11    analysis = generator(analysis_prompt, max_length=50, do_sample=False)[0]['generated_text']12 13    # Step 2: Decide what to do (simple rule-based agent)14    if "summarize" in user_input.lower():15        task_prompt = f"Summarize this text in 2 lines: {user_input}"16    elif "question" in user_input.lower() or "?" in user_input:17        task_prompt = f"Answer this question briefly: {user_input}"18    else:19        task_prompt = f"Generate a helpful response: {user_input}"20 21    # Step 3: LLM Response22    response = generator(task_prompt, max_length=80, do_sample=False)[0]['generated_text']23 24    # Step 4: Return both analysis + final response25    return f"🔎 Agent Analysis: {analysis}\n\n💡 Agent Response: {response}"26 27 28# Gradio UI29demo = gr.Interface(30    fn=agentic_ai,31    inputs=gr.Textbox(lines=3, placeholder="Type your text here..."),32    outputs="text",33    title="🤖 Mini Agentic LLM App",34    description="Smallest free demo of an Agentic AI using NLP + LLM on Hugging Face & Gradio. Input few lines or paragraph with question and Click Submit"35)36 37if __name__ == "__main__":38    demo.launch()39