import os
import asyncio
import gradio as gr
from browser_use import Agent

# NATIVE HUGGING FACE URL RESOLVER
try:
    from huggingface_hub import get_space_runtime
    runtime = get_space_runtime(os.environ.get("SPACE_ID", ""))
    worldwide_url = f"https://huggingface.co/spaces/{os.environ.get('SPACE_ID', '')}"
except Exception:
    space_id = os.environ.get("SPACE_ID", "username/space-name")
    try:
        username, space_name = space_id.split("/")
        clean_user = username.lower().replace("_", "-")
        clean_space = space_name.lower().replace("_", "-")
        worldwide_url = f"https://{clean_user}-{clean_space}.hf.space"
    except ValueError:
        worldwide_url = "https://huggingface.co/spaces"

# Print the link directly into your Hugging Face terminal logs on startup
print("\n" + "="*60)
print(f"🚀 WORLDWIDE PUBLIC URL AVAILABLE AT:")
print(f"🔗 {worldwide_url}")
print("="*60 + "\n")

async def ryusei_study_session(topic):
    if not topic.strip():
        yield "⚠️ Please enter a topic you want to learn!"
        return
        
    yield f"🔄 Ryusei is launching an advanced browser session to study '{topic}'..."

    # Securely fetch your Hugging Face API Token from your Space settings
    hf_token = os.environ.get("HF_TOKEN")
    if not hf_token:
        yield "❌ Error: HF_TOKEN secret is missing in Space Settings. Please add your token under Secrets."
        return

    try:
        # Load the base serverless endpoint along with the Chat Wrapper wrapper
        from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
        
        base_llm = HuggingFaceEndpoint(
            repo_id="Qwen/Qwen2.5-7B-Instruct",
            task="text-generation",
            max_new_tokens=1500,
            temperature=0.1,
            huggingfacehub_api_token=hf_token
        )
        
        # FIX: Wrap the endpoint in ChatHuggingFace so it populates 'model_name' for browser-use
        llm = ChatHuggingFace(llm=base_llm)

        study_prompt = f"""
        You are an elite academic tutor agent. The student wants to study and learn about: "{topic}".
        
        Execute these precise structural tasks:
        1. Go to a search engine (like DuckDuckGo) and search for information on "{topic}".
        2. Visit at least two separate relevant resource web links or encyclopedia entries.
        3. Read the contents, filter out promotional clutter, and synthesize the educational points.
        4. Output a clear, structured learning guide based on your findings.
        
        Your final response must be formatted in clean Markdown with these sections:
        # 📚 Master Lesson: {topic}
        
        ### 💡 Simple Analogy
        *(Explain the concept like I am a complete beginner using a relatable comparison)*
        
        ### 🔍 Core Principles Breakdown
        *(Provide clear bullet points explaining the most crucial functional components)*
        
        ### 🛠️ Practical Application
        *(Give a real-world example of how this topic applies or works in active industries)*
        """

        # Let the agent auto-initialize its own hidden browser internally using the wrapped chat llm
        agent = Agent(
            task=study_prompt,
            llm=llm
        )
        
        # Run the automated browser routine in the cloud space environment
        history = await agent.run()
        
        # Yield the final compiled structured study guide
        yield history.final_result()

    except Exception as e:
        yield f"⚠️ Studio Agent ran into an execution error: {str(e)}\n\nMake sure your HF_TOKEN is valid and your Space has internet access enabled."

# Set up the visual Gradio Web Dashboard interface
with gr.Blocks() as demo:
    gr.HTML(f"""
    <div style="background-color: #2e7d32; color: white; padding: 15px; text-align: center; border-radius: 8px; font-family: sans-serif; margin-bottom: 20px;">
        <span style="font-size: 1.2em; font-weight: bold;">🌍 Your Worldwide Web App Link Is Live!</span><br>
        <span style="font-size: 0.95em;">Share this direct link with anyone in the world:</span><br>
        <a href="{worldwide_url}" target="_blank" style="color: #a3e635; font-weight: bold; text-decoration: underline; font-size: 1.1em;">{worldwide_url}</a>
    </div>
    """)

    gr.Markdown("# 🎓 Ryusei: Advanced Autonomous Research Tutor (HF Engine)")
    gr.Markdown("An autonomous agent that drives a cloud browser using Hugging Face Hub models to research text layouts.")
    
    with gr.Row():
        topic_input = gr.Textbox(
            label="What concept, technology, or school topic do you want to learn about?",
            placeholder="e.g., How computer RAM stores temporary variables, or the process of cellular mitosis."
        )
    
    launch_btn = gr.Button("Start Researching", variant="primary")
    output_panel = gr.Markdown()
    
    launch_btn.click(fn=ryusei_study_session, inputs=topic_input, outputs=output_panel)

# Launch the Gradio dashboard app
demo.queue().launch(theme=gr.themes.Default())