app.py

from PIL import Image, ImageDraw, ImageFont
from dotenv import load_dotenv
import matplotlib.pyplot as plt
from io import BytesIO
import gradio as gr
import numpy as np
import requests
import base64
import boto3
import uuid
import os
import io

load_dotenv()
AWS_ACCESS_KEY_ID = os.environ.get('AWS_ACCESS_KEY_ID')
AWS_SECRET_ACCESS_KEY = os.environ.get('AWS_SECRET_ACCESS_KEY')
s3 = boto3.client('s3',
                  aws_access_key_id=AWS_ACCESS_KEY_ID,
                  aws_secret_access_key=AWS_SECRET_ACCESS_KEY)

   
def upload2aws(img_array): 
    image = Image.fromarray(img_array)
    buffer = io.BytesIO()
    image.save(buffer, format='JPEG')
    buffer.seek(0)
    unique_name = str(uuid.uuid4())
    s3.put_object(Bucket='predict-packages', Key=f'images_webapp_counters/{unique_name}.jpg', Body=buffer)
    return None

def send2api(input_img, api_url):
    buf = io.BytesIO()
    plt.imsave(buf, input_img, format='jpg')
    files = {'image': buf.getvalue()}
    res = requests.post(api_url, files=files)
    try:
        res.raise_for_status()
        if res.status_code != 204:
            response = res.json()
    except Exception as e:
        print(str(e))
    return response

def displaytext_detclasim(c_cnames, c_scinames, coverage):
    countings_list = list(c_scinames.items())
    countings_list.sort(key = lambda x: x[1], reverse=True)
    total = 0
    for (_,c) in countings_list:
        total += c
    
    text = f'coverage = {coverage}'+'\n\n'
    text += 'Countings by scientific name:\n'
    countings_list = list(c_scinames.items())
    countings_list.sort(key = lambda x: x[1], reverse=True)
    for key,value in countings_list:
      text += f'{key} = {value}'+'\n'
    text += '\n\n'
    text += 'Countings by common name:\n'

    countings_list = list(c_cnames.items())
    countings_list.sort(key = lambda x: x[1], reverse=True)
    for key,value in countings_list:
      text += f'{key} = {value}'+'\n'
    text += '\n'
    text += f'total = {total}'+'\n'
    return text

def displaytext_yolocounter(countings, coverage):
    countings_list = list(countings.items())
    countings_list.sort(key = lambda x: x[1], reverse=True)
    total = 0
    for (y_class,c) in countings_list:
        total += c

    text = f'coverage = {coverage}'+'\n\n'
    for key,value in countings_list:
        text += f'{key} = {value}'+'\n'
    text += '\n'
    text += f'total = {total}'+'\n'
    return text

def display_detectionsandcountings_directcounter(img_array, countings, prob_th=0, cth = 0):
    img = Image.fromarray(img_array)
    img1 = ImageDraw.Draw(img)
    h, w = img.size
    ratio = h/4000

    countings_list = list(countings.items())
    countings_list.sort(key = lambda x: x[1], reverse=True)
    yi=int(20*ratio)
    total = 0
    for (y_class,c) in countings_list:
        if c > cth:
            img1.text((int(50*ratio), yi), "# {} = {}".format(y_class, c), fill='red')
            yi += int(100*ratio)
            total += c
    yi += int(100*ratio)
    img1.text((int(50*ratio), yi), "# {} = {}".format('total', total), fill='red')

    text = ''
    for key,value in countings_list:
        text += f'{key} = {value}'+'\n'
    text += '\n'
    text += f'total = {total}'+'\n'
    return img, text

def testing_countingid(input_img):
    upload2aws(input_img)
    
    api_url = 'http://countingid-test.us-east-1.elasticbeanstalk.com/predict'
    response = send2api(input_img, api_url)
    c_cnames = response['countings_cnames']
    c_scinames = response['countings_scinames']
    coverage = response['coverage']
    detections = response['detections']
    img_out = response['img_out']
    img = Image.open(BytesIO(base64.b64decode(img_out)))
    _, text = displaytext_detclasim(c_cnames, c_scinames, coverage)
    return img, text

def testing_yolocounter(input_img):
    api_url = 'http://yolocounter-test.us-east-1.elasticbeanstalk.com/predict'
    response = send2api(input_img, api_url)
    countings = response['countings_scinames']
    coverage = response['coverage']
    detections = response['detections']
    img_out = response['img_out']
    img = Image.open(BytesIO(base64.b64decode(img_out)))
    text = displaytext_yolocounter(countings, coverage)
    return img, text

def testing_directcounter(input_img):
    api_url = 'http://directcounter-test.us-east-1.elasticbeanstalk.com/predict'
    response = send2api(input_img, api_url)
    countings = response['countings_scinames']
    img, text = display_detectionsandcountings_directcounter(input_img, countings, prob_th=0, cth = 0)
    return img, text

with gr.Blocks() as demo:
    gr.Markdown("Submit an image with insects in a trap")

    with gr.Tab("Species & Common Name Count"):
        with gr.Row():
            input1 = gr.Image()
            output1 =[gr.Image().style(height=500, width=500), gr.Textbox(lines=20)]
        button1 = gr.Button("Submit")
    button1.click(testing_countingid, input1, output1)

    with gr.Tab("Simplified Scientific Name Count"):
        with gr.Row():
            #input2 = gr.Image()
            output2 =[gr.Image().style(height=500, width=500), gr.Textbox(lines=20)]
        #button2 = gr.Button("Submit")
    button1.click(testing_yolocounter, input1, output2)

"""    with gr.Tab("Direct insect counter"):
        with gr.Row():
            #input3 = gr.Image()
            output3 =[gr.Image().style(height=500, width=500), gr.Textbox(lines=20)]
        #button3 = gr.Button("Submit")
    button1.click(testing_directcounter, input1, output3)
"""

demo.launch()