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}"


# Tutorial text (20 bullets)
tutorial_text = """
---

## 📘 How to Use this Web App (Step by Step)

1. Open the app on Hugging Face.  
2. You’ll see a text box in the center.  
3. Type your question, statement, or text.  
4. Click the orange **Submit** button.  
5. The AI will analyze your input first.  
6. You will see **Agent Analysis** (what AI thinks you mean).  
7. You will see **Agent Response** (the final helpful reply).  
8. If you ask a question (with “?”), AI gives an answer.  
9. If you ask to **summarize**, AI will summarize in 2 lines.  
10. If you type general text, AI will generate a response.  
11. Use it for **career guidance** (“What skills for AI engineer?”).  
12. Use it for **Q&A** (“What is blockchain in 2 lines?”).  
13. Use it for **summarization** (“Summarize AI benefits in education”).  
14. Use it for **decision help** (“AI or Cloud — which first?”).  
15. You can type motivational requests (“I feel stressed”).  
16. The agent is lightweight, runs free on Hugging Face.  
17. No login required — runs directly in your browser.  
18. Works best with short, clear sentences.  
19. Try experimenting with multiple topics.  
20. Share the app link with friends & colleagues 🚀.  

---
"""

# Gradio UI with example + tutorial under output
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 Mini Agentic LLM App")
    gr.Markdown("Smallest free demo of an Agentic AI using NLP + LLM on Hugging Face & Gradio.")

    with gr.Row():
        user_input = gr.Textbox(lines=3, placeholder="Type your text here...")
    output = gr.Textbox(label="AI Output")

    # Button
    run_btn = gr.Button("🚀 Run")

    # Always run with example at startup
    demo.load(fn=agentic_ai, inputs=[gr.Textbox(value="Summarize AI in one line", visible=False)], outputs=output)

    run_btn.click(agentic_ai, inputs=user_input, outputs=output)

    # Tutorial under the app
    gr.Markdown(tutorial_text)

if __name__ == "__main__":
    demo.launch()