main.py

from fastapi import FastAPI, Form, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, FileResponse
from diffusers import StableDiffusionPipeline
import torch
import uuid
import base64
import io
from PIL import Image
import os

# Initialize FastAPI app
app = FastAPI(title="PromptAgro Image Generator API")

# Add CORS middleware to allow frontend connections
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, specify your frontend domains
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load Stable Diffusion LCM model (your original approach)
print("🚀 Loading Stable Diffusion Model...")
model_id = "rupeshs/LCM-runwayml-stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
print("✅ Model Loaded.")

@app.get("/")
async def root():
    """Health check endpoint"""
    return {
        "status": "alive",
        "service": "PromptAgro Image Generator",
        "model_loaded": True,
        "device": "cuda" if torch.cuda.is_available() else "cpu"
    }

@app.post("/generate/")
async def generate_image(prompt: str = Form(...)):
    """
    Generate product packaging image from input prompt.
    Returns image file directly (your original approach).
    """
    print(f"🖌️ Generating image for prompt: {prompt}")

    # Generate image (your original approach)
    image = pipe(prompt).images[0]

    # Save image to temp file (your original approach)
    filename = f"/tmp/{uuid.uuid4().hex}.png"
    image.save(filename)

    print(f"📦 Image saved to {filename}")

    # Return image file as response (your original approach)
    return FileResponse(filename, media_type="image/png")

@app.post("/generate-json/")
async def generate_image_json(
    prompt: str = Form(...),
    width: int = Form(512),
    height: int = Form(512),
    num_inference_steps: int = Form(4),  # LCM works well with few steps
    guidance_scale: float = Form(1.0)    # LCM uses low guidance
):
    """
    Generate image and return as JSON with base64 data (for frontend integration).
    """
    print(f"🖌️ Generating image for prompt: {prompt}")
    
    try:
        # Generate image with parameters optimized for LCM
        image = pipe(
            prompt=prompt,
            width=width,
            height=height,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale
        ).images[0]
        
        # Convert image to base64 for JSON response
        buffer = io.BytesIO()
        image.save(buffer, format='PNG')
        img_str = base64.b64encode(buffer.getvalue()).decode()
        
        print("✅ Image generated successfully")
        
        return JSONResponse({
            "success": True,
            "image_data": f"data:image/png;base64,{img_str}",
            "prompt_used": prompt,
            "dimensions": {"width": width, "height": height},
            "steps": num_inference_steps
        })
        
    except Exception as e:
        print(f"❌ Generation failed: {e}")
        raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")

@app.post("/generate-packaging/")
async def generate_packaging_specific(
    product_name: str = Form(...),
    colors: str = Form("green,yellow"),
    emotion: str = Form("trust"),
    platform: str = Form("farmers-market")
):
    """
    Generate packaging with PromptAgro-specific prompt engineering
    """
    # Create professional prompt for agricultural packaging
    prompt = f"""Professional agricultural product packaging design for {product_name}, 
    modern clean style, {colors.replace(',', ' and ')} color scheme, premium typography, 
    conveying {emotion}, suitable for {platform}, product photography style, 
    white background, high quality commercial design, realistic packaging mockup, 
    professional studio lighting, eco-friendly agricultural branding"""
    
    prompt = prompt.strip().replace('\n', ' ').replace('  ', ' ')
    
    print(f"🎨 Generating packaging for: {product_name}")
    print(f"📝 Using prompt: {prompt}")
    
    try:
        # Generate with packaging-optimized settings
        image = pipe(
            prompt=prompt,
            width=768,
            height=768,
            num_inference_steps=6,
            guidance_scale=1.5
        ).images[0]
        
        # Convert to base64
        buffer = io.BytesIO()
        image.save(buffer, format='PNG')
        img_str = base64.b64encode(buffer.getvalue()).decode()
        
        return JSONResponse({
            "success": True,
            "image_data": f"data:image/png;base64,{img_str}",
            "prompt_used": prompt,
            "product_name": product_name,
            "generator": "Stable Diffusion LCM",
            "cost": "FREE",
            "processing_time": "~3-5 seconds"
        })
        
    except Exception as e:
        print(f"❌ Packaging generation failed: {e}")
        raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)