main.py

from fastapi import FastAPI, HTTPException, BackgroundTasks, Depends
from pydantic import BaseModel
from fastapi.concurrency import run_in_threadpool
from fastapi.middleware.cors import CORSMiddleware
import uuid
import tempfile
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
import os

# Set the Hugging Face cache directory to a writable location
os.environ['HF_HOME'] = '/tmp/huggingface_cache'

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 = {}

# Use a temporary directory for storing images
image_storage_dir = tempfile.mkdtemp()
print(f"Temporary directory for images: {image_storage_dir}")

# Dictionary to store images temporarily
image_store = {}

@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):
    def process_request():
        chat_history = get_chat_history(request.chat_id)
        prompt = f"""
        You are a professional assistant responsible for crafting a clear and visually compelling prompt for an image generation model. Your task is to generate a high-quality prompt for creating both the **main subject** and the **background** of the image.

        Image Specifications:
        - **Subject**: Focus on **{request.subject}**, highlighting its defining features, expressions, and textures.
        - **Style**: Emphasize the **{request.style}**, capturing its key characteristics.
        - **Background**: Create a background with **{request.background_details}** that complements and enhances the subject. Ensure it aligns with the color theme and overall composition.
        - **Camera and Lighting**:
          - Lighting: Apply **{request.lighting_conditions}**, emphasizing depth, highlights, and shadows to accentuate the subject and harmonize the background.
        - **Framing**: Use a **{request.framing_style}** to enhance the composition around both the subject and the background.
        - **Materials**: Highlight textures like **{request.material_details}**, with realistic details and natural imperfections on the subject and background.
        - **Key Elements**: Include **{request.elements}** to enrich the subject’s details and add cohesive elements to the background.
        - **Color Theme**: Follow the **{request.color_theme}** to set the mood and tone for the entire scene.
        - Negative Prompt: Avoid grainy, blurry, or deformed outputs.
        - **Text to Include in Image**: Clearly display the text **"{request.text}"** as part of the composition (e.g., on a card, badge, or banner) attached to the subject in a realistic and contextually appropriate way.
        """
        refined_prompt = llm.invoke(prompt).content.strip()

        # Save the prompt in MongoDB
        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})

        # API call to image generation service
        url = "https://api.bfl.ml/v1/flux-pro-1.1"
        headers = {
            "accept": "application/json",
            "x-key": "4f69d408-4979-4812-8ad2-ec6d232c9ddf",
            "Content-Type": "application/json"
        }
        payload = {
            "prompt": refined_prompt,
            "width": 1024,
            "height": 1024,
            "guidance_scale": 1,
            "num_inference_steps": 50,
            "max_sequence_length": 512,
        }

        # Initial request to generate the image
        response = requests.post(url, headers=headers, json=payload).json()
        if "id" not in response:
            raise HTTPException(status_code=500, detail="Error generating image: ID missing from response")

        request_id = response["id"]

        # Poll for the image result
        while True:
            time.sleep(0.5)
            result = requests.get(
                "https://api.bfl.ml/v1/get_result",
                headers=headers,
                params={"id": request_id},
            ).json()

            if result["status"] == "Ready":
                if "result" in result and "sample" in result["result"]:
                    image_url = result["result"]["sample"]

                    # Download the image
                    image_response = requests.get(image_url)
                    if image_response.status_code == 200:
                        img = Image.open(BytesIO(image_response.content))
                        filename = f"generated_{uuid.uuid4()}.png"
                        filepath = save_image_locally(img, filename)
                        return filepath
                    else:
                        raise HTTPException(status_code=500, detail="Failed to download the image")
                else:
                    raise HTTPException(status_code=500, detail="Expected 'sample' key not found in the result")
            elif result["status"] == "Error":
                raise HTTPException(status_code=500, detail=f"Image generation failed: {result.get('error', 'Unknown error')}")

    # Run the request processing in a thread
    future = executor.submit(process_request)
    filepath = await run_in_threadpool(future.result)

    return {
        "status": "Image generated successfully",
        "file_path": filepath,
    }


@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.get("/")
async def root():
    return {"message": "API is up and running!"}