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from fastapi import FastAPI, HTTPException, BackgroundTasks, Depends
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
import uuid
from concurrent.futures import ThreadPoolExecutor
from pymongo import MongoClient
from urllib.parse import quote_plus
from langchain_groq import ChatGroq
from aura_sr import AuraSR
from io import BytesIO
from PIL import Image
import requests
import os
app = FastAPI()
# Middleware for CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# Globals
executor = ThreadPoolExecutor(max_workers=10)
llm = None
upscale_model = None
client = MongoClient(f"mongodb+srv://hammad:{quote_plus('momimaad@123')}@cluster0.2a9yu.mongodb.net/")
db = client["Flux"]
collection = db["chat_histories"]
chat_sessions = {}
image_storage_dir = "./images" # Directory to save images locally
# Ensure the image storage directory exists
os.makedirs(image_storage_dir, exist_ok=True)
@app.on_event("startup")
async def startup():
global llm, upscale_model
llm = ChatGroq(
model="llama-3.3-70b-versatile",
temperature=0.7,
max_tokens=1024,
api_key="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",
)
upscale_model = AuraSR.from_pretrained("fal/AuraSR-v2")
@app.on_event("shutdown")
def shutdown():
client.close()
executor.shutdown()
# Pydantic models
class ImageRequest(BaseModel):
subject: str
style: str
color_theme: str
elements: str
color_mode: str
lighting_conditions: str
framing_style: str
material_details: str
text: str
background_details: str
user_prompt: str
chat_id: str
# Helper Functions
def generate_chat_id():
chat_id = str(uuid.uuid4())
chat_sessions[chat_id] = collection
return chat_id
def get_chat_history(chat_id):
messages = collection.find({"session_id": chat_id})
return "\n".join(
f"User: {msg['content']}" if msg['role'] == "user" else f"AI: {msg['content']}"
for msg in messages
)
def save_image_locally(image, filename):
filepath = os.path.join(image_storage_dir, filename)
image.save(filepath, format="PNG")
return filepath
# Endpoints
@app.post("/new-chat", response_model=dict)
async def new_chat():
chat_id = generate_chat_id()
return {"chat_id": chat_id}
@app.post("/generate-image", response_model=dict)
async def generate_image(request: ImageRequest, background_tasks: BackgroundTasks):
def process_request():
chat_history = get_chat_history(request.chat_id)
prompt = f"""
Subject: {request.subject}
Style: {request.style}
...
Chat History: {chat_history}
User Prompt: {request.user_prompt}
"""
refined_prompt = llm.invoke(prompt).content.strip()
collection.insert_one({"session_id": request.chat_id, "role": "user", "content": request.user_prompt})
collection.insert_one({"session_id": request.chat_id, "role": "ai", "content": refined_prompt})
# Simulate image generation
response = requests.post(
"https://api.bfl.ml/v1/flux-pro-1.1",
json={"prompt": refined_prompt}
).json()
image_url = response["result"]["sample"]
# Download and save the image locally
image_response = requests.get(image_url)
img = Image.open(BytesIO(image_response.content))
filename = f"generated_{uuid.uuid4()}.png"
filepath = save_image_locally(img, filename)
return filepath
task = executor.submit(process_request)
background_tasks.add_task(task)
return {"status": "Processing"}
@app.post("/upscale-image", response_model=dict)
async def upscale_image(image_url: str, background_tasks: BackgroundTasks):
def process_image():
response = requests.get(image_url)
img = Image.open(BytesIO(response.content))
upscaled_image = upscale_model.upscale_4x_overlapped(img)
filename = f"upscaled_{uuid.uuid4()}.png"
filepath = save_image_locally(upscaled_image, filename)
return filepath
task = executor.submit(process_image)
background_tasks.add_task(task)
return {"status": "Processing"}
@app.post("/set-prompt", response_model=dict)
async def set_prompt(chat_id: str, user_prompt: str):
chat_history = get_chat_history(chat_id)
refined_prompt = llm.invoke(f"{chat_history}\nUser Prompt: {user_prompt}").content.strip()
collection.insert_one({"session_id": chat_id, "role": "user", "content": user_prompt})
collection.insert_one({"session_id": chat_id, "role": "ai", "content": refined_prompt})
return {"refined_prompt": refined_prompt}
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