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
import server
#http://localhost:8000
app = FastAPI()
# Root route
@app.get('/')
def main():
return "Hello World taha"
@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_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_processed_image.png")
@app.post('/predict2')
async def predict2(supported_types_str: str = Form(...), age: str = Form(...), file: UploadFile = File(...)):
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 BytesIO object
img_byte_arr = io.BytesIO()
processed_image.save(img_byte_arr, format='PNG')
img_byte_arr.seek(0) # Move to the beginning of the BytesIO buffer
# Create an HTML response with the processed image
html_content = f"""
Processed Image:
.decode('latin1')})
Your name: {supported_types_str}
Your age: {age}
Download Processed Image
"""
return HTMLResponse(content=html_content)