app.py

import gradio as gr
from fastapi import FastAPI, UploadFile, File, Request
from fastapi.responses import FileResponse, HTMLResponse
import uuid
import os
import json
from datetime import datetime
from typing import Dict, List
import shutil
import asyncio
from contextlib import asynccontextmanager

# Initialize data storage
peers: Dict[str, Dict] = {}
jobs: List[Dict] = []

# Create directories
os.makedirs("results", exist_ok=True)
os.makedirs("client", exist_ok=True)

# Client code
CLIENT_CODE = '''import requests
import subprocess
import time
import os
import sys
from datetime import datetime

# Configuration
PEER_ID = f"peer-{os.getenv('COMPUTERNAME', 'unknown')}-{datetime.now().strftime('%Y%m%d%H%M%S')}"
SERVER_URL = "https://your-username-your-space.hf.space"  # Replace with actual Space URL

def check_gpu():
    """Check GPU availability"""
    try:
        result = subprocess.run(['nvidia-smi', '--query-gpu=utilization.gpu', '--format=csv,noheader,nounits'], 
                              capture_output=True, text=True)
        if result.returncode == 0:
            gpu_usage = int(result.stdout.strip())
            return gpu_usage < 20  # GPU is idle if usage < 20%
    except:
        print("GPU not found. Running in CPU mode.")
        return False

def register_peer():
    """Register peer with server"""
    try:
        response = requests.post(f"{SERVER_URL}/api/peers/register", params={"peer_id": PEER_ID})
        if response.status_code == 200:
            print(f"✅ Peer registered: {PEER_ID}")
            return True
    except Exception as e:
        print(f"❌ Server connection failed: {e}")
    return False

def generate_image_cpu(prompt, output_path):
    """Generate test image using CPU"""
    from PIL import Image, ImageDraw, ImageFont
    
    img = Image.new('RGB', (512, 512), color='white')
    draw = ImageDraw.Draw(img)
    
    # Draw prompt text
    text = f"Prompt: {prompt[:50]}..."
    draw.text((10, 10), text, fill='black')
    draw.text((10, 40), f"Generated by: {PEER_ID}", fill='gray')
    draw.text((10, 70), f"Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", fill='gray')
    
    img.save(output_path)
    print(f"📝 Test image generated: {output_path}")

def main():
    print("🚀 Starting P2P GPU Client...")
    
    if not register_peer():
        print("Server registration failed. Exiting.")
        return
    
    while True:
        try:
            # Heartbeat
            requests.post(f"{SERVER_URL}/api/peers/heartbeat", params={"peer_id": PEER_ID})
            
            # Request job
            response = requests.get(f"{SERVER_URL}/api/jobs/request", params={"peer_id": PEER_ID})
            if response.status_code == 200:
                job_data = response.json()
                
                if job_data.get("job"):
                    job = job_data["job"]
                    job_id = job["id"]
                    prompt = job["prompt"]
                    
                    print(f"\\n📋 New job received: {prompt}")
                    
                    # Generate image
                    output_path = f"{job_id}.png"
                    
                    if check_gpu():
                        print("🎮 Generating with GPU...")
                        # Actual GPU generation code would go here
                        generate_image_cpu(prompt, output_path)
                    else:
                        print("💻 Generating with CPU...")
                        generate_image_cpu(prompt, output_path)
                    
                    # Upload result
                    with open(output_path, 'rb') as f:
                        files = {'file': (output_path, f, 'image/png')}
                        response = requests.post(
                            f"{SERVER_URL}/api/jobs/result",
                            params={"job_id": job_id},
                            files=files
                        )
                    
                    if response.status_code == 200:
                        print("✅ Result uploaded successfully")
                    
                    # Clean up
                    os.remove(output_path)
            
            time.sleep(10)  # Check every 10 seconds
            
        except KeyboardInterrupt:
            print("\\n👋 Shutting down")
            break
        except Exception as e:
            print(f"⚠️ Error: {e}")
            time.sleep(30)

if __name__ == "__main__":
    # Check required packages
    try:
        import PIL
    except ImportError:
        print("Installing required packages...")
        subprocess.run([sys.executable, "-m", "pip", "install", "pillow", "requests"])
    
    main()
'''

# Create client files
with open("client/peer_agent.py", "w", encoding="utf-8") as f:
    f.write(CLIENT_CODE)

with open("client/requirements.txt", "w") as f:
    f.write("requests\npillow\n")

with open("client/README.md", "w", encoding="utf-8") as f:
    f.write("""# P2P GPU Client for Windows

## Installation
1. Install Python 3.8+
2. Run `pip install -r requirements.txt`
3. Update SERVER_URL in `peer_agent.py` with actual Hugging Face Space URL
4. Run `python peer_agent.py`

## GPU Support
- Automatically detects NVIDIA GPU if available
- Falls back to CPU mode for testing
""")

# FastAPI app with lifespan
@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup
    print("Starting P2P GPU Hub...")
    yield
    # Shutdown
    print("Shutting down P2P GPU Hub...")

app = FastAPI(lifespan=lifespan)

# API endpoints
@app.get("/api/status")
async def get_status():
    """Get system status"""
    active_peers = sum(1 for p in peers.values() 
                      if (datetime.now() - p['last_seen']).seconds < 60)
    pending_jobs = sum(1 for j in jobs if j['status'] == 'pending')
    completed_jobs = sum(1 for j in jobs if j['status'] == 'completed')
    
    recent_results = [
        {"filename": j['filename'], "prompt": j['prompt']} 
        for j in jobs[-10:] if j['status'] == 'completed' and 'filename' in j
    ]
    
    return {
        "active_peers": active_peers,
        "pending_jobs": pending_jobs,
        "completed_jobs": completed_jobs,
        "recent_results": recent_results
    }

@app.post("/api/peers/register")
async def register_peer(peer_id: str):
    """Register a peer"""
    peers[peer_id] = {
        "status": "idle",
        "last_seen": datetime.now(),
        "jobs_completed": 0
    }
    return {"status": "registered", "peer_id": peer_id}

@app.post("/api/peers/heartbeat")
async def heartbeat(peer_id: str):
    """Update peer status"""
    if peer_id in peers:
        peers[peer_id]["last_seen"] = datetime.now()
        return {"status": "alive"}
    return {"status": "unregistered"}

@app.post("/api/jobs/submit")
async def submit_job(request: Request):
    """Submit a job"""
    data = await request.json()
    job_id = str(uuid.uuid4())
    job = {
        "id": job_id,
        "prompt": data.get("prompt", ""),
        "status": "pending",
        "created_at": datetime.now()
    }
    jobs.append(job)
    return {"job_id": job_id, "status": "submitted"}

@app.get("/api/jobs/request")
async def request_job(peer_id: str):
    """Request a job for processing"""
    for job in jobs:
        if job["status"] == "pending":
            job["status"] = "assigned"
            job["peer_id"] = peer_id
            job["assigned_at"] = datetime.now()
            return {"job": job}
    
    return {"job": None}

@app.post("/api/jobs/result")
async def submit_result(job_id: str, file: UploadFile = File(...)):
    """Submit job result"""
    filename = f"{job_id}.png"
    file_path = f"results/{filename}"
    
    with open(file_path, "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)
    
    for job in jobs:
        if job["id"] == job_id:
            job["status"] = "completed"
            job["filename"] = filename
            job["completed_at"] = datetime.now()
            
            if "peer_id" in job and job["peer_id"] in peers:
                peers[job["peer_id"]]["jobs_completed"] += 1
            break
    
    return {"status": "success", "filename": filename}

@app.get("/api/results/{filename}")
async def get_result(filename: str):
    """Get generated image"""
    file_path = f"results/{filename}"
    if os.path.exists(file_path):
        return FileResponse(file_path)
    return {"error": "File not found"}

@app.get("/api/client/{filename}")
async def get_client_file(filename: str):
    """Download client file"""
    file_path = f"client/{filename}"
    if os.path.exists(file_path):
        return FileResponse(file_path, filename=filename)
    return {"error": "File not found"}

# Gradio interface functions
def gradio_submit_job(prompt):
    """Submit job through Gradio"""
    if not prompt:
        return "Please enter a prompt"
    
    job_id = str(uuid.uuid4())
    job = {
        "id": job_id,
        "prompt": prompt,
        "status": "pending",
        "created_at": datetime.now()
    }
    jobs.append(job)
    return f"Job submitted successfully! Job ID: {job_id}"

def gradio_get_status():
    """Get status through Gradio"""
    active_peers = sum(1 for p in peers.values() 
                      if (datetime.now() - p['last_seen']).seconds < 60)
    pending = sum(1 for j in jobs if j['status'] == 'pending')
    completed = sum(1 for j in jobs if j['status'] == 'completed')
    
    status_text = f"""### System Status
- Active Peers: {active_peers}
- Pending Jobs: {pending}
- Completed Jobs: {completed}

### Recent Jobs
"""
    
    # Add recent jobs
    recent_jobs = jobs[-5:][::-1]  # Last 5 jobs, reversed
    for job in recent_jobs:
        status_text += f"\n- **{job['id'][:8]}...**: {job['prompt'][:50]}... ({job['status']})"
    
    return status_text

def gradio_get_gallery():
    """Get completed images for gallery"""
    image_files = []
    for job in jobs[-20:]:  # Last 20 jobs
        if job['status'] == 'completed' and 'filename' in job:
            file_path = f"results/{job['filename']}"
            if os.path.exists(file_path):
                image_files.append((file_path, job['prompt']))
    
    return image_files

# Create Gradio interface
with gr.Blocks(title="P2P GPU Image Generation Hub") as demo:
    gr.Markdown("# 🤖 P2P GPU Image Generation Hub")
    gr.Markdown("Distributed image generation using idle GPUs from peer nodes")
    
    with gr.Tabs():
        with gr.Tab("Submit Job"):
            with gr.Row():
                with gr.Column():
                    prompt_input = gr.Textbox(
                        label="Image Prompt", 
                        placeholder="Describe the image you want to generate...",
                        lines=3
                    )
                    submit_btn = gr.Button("Submit Job", variant="primary")
                    result_text = gr.Textbox(label="Result", interactive=False)
                    
                    submit_btn.click(
                        fn=gradio_submit_job, 
                        inputs=prompt_input, 
                        outputs=result_text
                    )
        
        with gr.Tab("System Status"):
            status_display = gr.Markdown()
            refresh_btn = gr.Button("Refresh Status")
            
            refresh_btn.click(
                fn=gradio_get_status, 
                outputs=status_display
            )
            
            # Auto-refresh status on load
            demo.load(fn=gradio_get_status, outputs=status_display)
        
        with gr.Tab("Gallery"):
            gallery = gr.Gallery(
                label="Generated Images",
                show_label=True,
                elem_id="gallery",
                columns=3,
                rows=2,
                height="auto"
            )
            refresh_gallery_btn = gr.Button("Refresh Gallery")
            
            refresh_gallery_btn.click(
                fn=gradio_get_gallery,
                outputs=gallery
            )
            
            # Auto-load gallery on tab load
            demo.load(fn=gradio_get_gallery, outputs=gallery)
        
        with gr.Tab("Download Client"):
            gr.Markdown("""
            ## Windows Client Setup
            
            1. Download the client files:
               - [peer_agent.py](/api/client/peer_agent.py)
               - [requirements.txt](/api/client/requirements.txt)
               - [README.md](/api/client/README.md)
            
            2. Install Python 3.8 or higher
            
            3. Install requirements:
               ```bash
               pip install -r requirements.txt
               ```
            
            4. Update the SERVER_URL in peer_agent.py with this Space's URL
            
            5. Run the client:
               ```bash
               python peer_agent.py
               ```
            
            The client will automatically detect GPU availability and start processing jobs.
            """)

# Mount Gradio app to FastAPI
app = gr.mount_gradio_app(app, demo, path="/")

# For Hugging Face Spaces
if __name__ == "__main__":
    demo.launch()