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# app.py
import os
import io
import base64
import numpy as np
from PIL import Image
import torch
from realesrgan import RealESRGANer
# FastAPI Libraries
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse, StreamingResponse
import uvicorn
import gradio as gr
# --- 1. Model Loading (Free Tier Optimized) ---
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Model will run on: {DEVICE}")
try:
# Real-ESRGAN ka lightweight, optimized model use kar rahe hain
model_path = RealESRGANer.model_path_from_name('RealESRGAN_x4plus')
# Model ko load karna (yeh memory mein rahega)
UPSCALER = RealESRGANer(
scale=4,
model_path=model_path,
dni_weight=None,
model_name='RealESRGAN_x4plus',
device=DEVICE
)
print("Real-ESRGAN model loaded successfully.")
except Exception as e:
print(f"ERROR: Model load nahi ho paya. Error: {e}")
UPSCALER = None
def run_upscaler(img_np: np.ndarray):
"""Core upscaling logic."""
if UPSCALER is None:
raise Exception("Model is not initialized.")
# Upscaling (yahan time lagta hai)
output_np, _ = UPSCALER.enhance(img_np, outscale=4)
return output_np
# --- 2. FastAPI Setup ---
# FastAPI application ko initialize karein
app = FastAPI(title="Real-ESRGAN Custom Upscaler API")
# --- 3. Custom API Endpoint ---
# Image file upload ke zariye upscaling
@app.post("/api/upscale/file")
async def upscale_image_api(image: UploadFile = File(...)):
"""
Image file ko upload karein aur 4x upscaled image wapas hasil karein.
"""
try:
# File ko PIL Image mein padhna
image_bytes = await image.read()
input_image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
# PIL image ko numpy array mein convert karna
img_np = np.array(input_image)
# Upscaling
output_np = run_upscaler(img_np)
# NumPy array ko wapas PIL Image mein convert karna
output_image = Image.fromarray(output_np)
# Image ko BytesIO mein save karna
img_io = io.BytesIO()
output_image.save(img_io, format='PNG')
img_io.seek(0)
# StreamingResponse se image ko wapas bhejna
return StreamingResponse(img_io, media_type="image/png")
except Exception as e:
return JSONResponse(status_code=500, content={"message": f"Processing error: {str(e)}"})
# --- 4. Gradio UI Integration ---
def upscale_for_gradio(input_image: Image.Image):
"""Gradio UI ke liye wrapper function."""
try:
img_np = np.array(input_image.convert("RGB"))
output_np = run_upscaler(img_np)
return Image.fromarray(output_np)
except Exception as e:
return f"Error: {str(e)}"
# Gradio Interface define karna
gr_interface = gr.Interface(
fn=upscale_for_gradio,
inputs=gr.Image(type="pil", label="Low-Resolution Image Upload Karein"),
outputs=gr.Image(type="pil", label="4x Upscaled (High-Quality) Image"),
title="⭐ Real-ESRGAN: AI Image Upscaler (UI & Custom API)",
description="Apni images ko 4x size mein badhayein. Yeh app Custom REST API aur Gradio UI dono offer karta hai.",
allow_flagging="never"
)
# Gradio ko FastAPI app mein mount karna
# '/gradio' path par UI available hoga
app = gr.mount_gradio_app(app, gr_interface, path="/")
# --- 5. Uvicorn Server Setup ---
# Yeh tabhi run hoga jab aap file ko directly chalayenge (lekin Docker mein yeh entry point hoga)
if __name__ == "__main__":
# Hugging Face Spaces Docker mein port 7860 par chalne ki umeed rakhta hai.
# Hamara server isi port par run hoga.
uvicorn.run(app, host="0.0.0.0", port=7860)
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