from fastapi import FastAPI ,Request ,Form, UploadFile, File
from fastapi.responses import JSONResponse
from fastapi.responses import HTMLResponse, FileResponse
import os
import io
from PIL import ImageOps,Image ,ImageFilter
#from transformers import pipeline
import matplotlib.pyplot as plt
import numpy as np
import ast
from server import *

#http://localhost:8000
app = FastAPI()

# Root route
@app.get('/')
def main():
    return "Hello World taha"

##### use space /tmp/ ...


@app.post('/imageStep1')
async def image_step1(image_file: UploadFile = File(...),type_of_filters: str = Form(...), blur_radius: str = Form(...),):
    
        contents = await image_file.read()
        image = Image.open(io.BytesIO(contents))
        
        produced_image=SegmenterBackground().Back_step1(image,type_of_filters,int(blur_radius))[0]

        # Save the processed image to a temporary file
        output_file_path_tmp = "/tmp/tmp_processed_image.png"
        produced_image.save(output_file_path_tmp)

        # Return the processed image for download
        return FileResponse(output_file_path_tmp, media_type='image/png', filename="/tmp/tmp_processed_image.png")
    





@app.post('/predict')
async  def predict(supported_types_str: str = Form(),age: str = Form() , file: UploadFile = File(...)): 
    # Form(...) to accept input as web form ,may change when android /upload 

    supported_types=ast.literal_eval(supported_types_str)

    contents = await file.read()
    image = Image.open(io.BytesIO(contents))
       
    # Process the image (example: convert to grayscale)
    processed_image = image.convert("L")

    # Save the processed image to a temporary file
    output_file_path = "/tmp/tmp_processed_image.png"
    processed_image.save(output_file_path)

    # Return the processed image for download
    return FileResponse(output_file_path, media_type='image/png', filename="/tmp/tmp_processed_image.png")