SyedUmairHasan/TextDetection

1import streamlit as st2from streamlit_drawable_canvas import st_canvas3import cv24import requests5import time6import os7 8st.title("Text Detection")9 10def signaturefunk():11    flag = False12    # Specify canvas parameters in application13    stroke_width = st.sidebar.slider("Stroke width: ", 5, 20, 6)14    stroke_color = st.sidebar.color_picker("Stroke color hex: ","#ffffff")15    # Create a canvas component16    canvas_result = st_canvas(17        fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity18        stroke_width=stroke_width,19        stroke_color=stroke_color,20        #background_color="#eee",21        background_color="black",22        height=250,23        drawing_mode='freedraw',24        display_toolbar=st.sidebar.checkbox("Display toolbar", True)25    )26 27    # Do something interesting with the image data and paths28    if st.button('Submit'):29        if canvas_result.image_data is not None:30            cv2.imwrite(f"img.jpg",  canvas_result.image_data)31            flag = True32 33 34    if flag == True:35        with open('img.jpg', 'rb') as f:36          data = f.read()37        r = requests.post(os.environ["endpoint"],data=data,headers={"Ocp-Apim-Subscription-Key":os.environ["key"],"Content-Type": "application/octet-stream"})38        time.sleep(3)39        r2 = requests.get(r.headers['Operation-Location'],headers={"Ocp-Apim-Subscription-Key":os.environ["key"]})40        my_dict = {}41        print(r2.json())42        for line in r2.json()['analyzeResult']['readResults'][0]['lines']:43          st.markdown(f"<h1 style='text-align:center;font-family:cursive;'>{line['text']}</h1>",unsafe_allow_html=True)44          for word in line['words']:45            my_dict[word['text']] = word['confidence']46        for key in my_dict:47            st.metric(label='',value=f"{key}", delta=f"{my_dict[key]*100} %")48            st.progress(my_dict[key])49        flag = False50 51 52signaturefunk()