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import gradio as gr
from transformers import pipeline
# Load small + free LLM
generator = pipeline("text2text-generation", model="google/flan-t5-small")
# --- Agent 1: Summarizer + Knowledge Graph Builder ---
def doc_agent(user_text):
# Step 1: Summarize
summary_prompt = f"Summarize this in 3 lines: {user_text}"
summary = generator(summary_prompt, max_length=80, do_sample=False)[0]['generated_text']
# Step 2: Build a "mini knowledge graph" (keywords as nodes)
keyword_prompt = f"Extract 5 important keywords from this text: {user_text}"
keywords = generator(keyword_prompt, max_length=40, do_sample=False)[0]['generated_text']
graph_nodes = [kw.strip() for kw in keywords.split(",") if kw.strip()]
graph_repr = " → ".join(graph_nodes) if graph_nodes else "No graph generated."
return f"📄 Summary:\n{summary}\n\n🕸️ Knowledge Graph:\n{graph_repr}"
# --- Agent 2: Career Skill Recommender ---
def career_agent(user_goal):
# Step 1: Analyze career intent
analysis_prompt = f"Identify skill gap for this career goal: {user_goal}"
analysis = generator(analysis_prompt, max_length=50, do_sample=False)[0]['generated_text']
# Step 2: Recommend roadmap
roadmap_prompt = f"Suggest a 3-step learning roadmap for: {user_goal}"
roadmap = generator(roadmap_prompt, max_length=80, do_sample=False)[0]['generated_text']
return f"🔍 Gap Analysis:\n{analysis}\n\n🛠️ Skill Roadmap:\n{roadmap}"
# --- Combined Agent Controller ---
def agentic_ai(user_input, mode):
if mode == "Document Insight":
return doc_agent(user_input)
elif mode == "Career Roadmap":
return career_agent(user_input)
else:
return "⚠️ Please choose a valid mode."
# --- Demo UI ---
demo = gr.Interface(
fn=agentic_ai,
inputs=[
gr.Textbox(lines=4, placeholder="Enter text or career goal..."),
gr.Radio(["Document Insight", "Career Roadmap"], label="Choose Mode")
],
outputs="text",
title="🌐 Mini Agentic AI MVP",
description="""
This smallest MVP demonstrates:
- 📄 Document Summarization
- 🕸️ Knowledge Graph (mini keyword graph)
- 🧑💻 Career Skill Gap Analysis
- 🛠️ Personalized 3-step Roadmap
Built with free Hugging Face + Gradio. Optimized for AI Research use cases.
"""
)
if __name__ == "__main__":
demo.launch()
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