Create app.py

app.py

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+import streamlit as st
+import keras_ocr
+import matplotlib.pyplot as plt
+from PIL import Image
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            .css-1wbqy5l {visibility: hidden;}
+            .css-15zrgzn {visibility: hidden;}
+            .css-klqnuk {visibility: hidden;}
+            .en6cib64 {visibility: hidden;}
+            .css-1u4fkce {visibility: hidden;}
+            .en6cib62 {visibility: hidden;}
+
+            .css-19rxjzo, .ef3psqc11 {
+            background-color: purple;
+            text-color: white;
+            }
+
+            div.stButton > button:first-child {
+            background-color: darkgreen;
+            text-weight: bold;
+            }
+            </style>
+            """
+st.markdown("<h1 style='text-align:center;'> Image Text Recognition</h1>", unsafe_allow_html=True)
+st.markdown("<h4 style='text-align:center;'> By Cyperts</h4>", unsafe_allow_html=True)
+st.markdown("---")
+st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+placeholder=st.empty()
+col1,col2=placeholder.columns(2)
+col1.image("example.png", width=250)
+col2.markdown("<p style='text-align:left;'> This is an example result</p>", unsafe_allow_html=True)
+
+# Read images from folder path to image object
+st.markdown("<h2>Upload an Image with Text</h2>",unsafe_allow_html=True)
+uploaded_img= st.file_uploader("Browse File", type=['jpeg','jpg','png'],accept_multiple_files=False)
+#container2=st.empty()
+
+def on_click():
+    if uploaded_img is not None:
+        pipeline = keras_ocr.pipeline.Pipeline()
+        print(uploaded_img.name)
+        images = [
+        keras_ocr.tools.read(img) for img in [uploaded_img
+                                        ]]
+        # generate text predictions from the images
+        prediction_groups = pipeline.recognize(images)
+        # plot the text predictions
+        fig, axs = plt.subplots(nrows=len(images), figsize=(10, 20))
+        #for ax, image, predictions in zip(axs, images, prediction_groups):
+        #keras_ocr.tools.drawAnnotations(image=images[0],predictions=prediction_groups[0], ax=axs)
+        keras_ocr.tools.drawAnnotations(image=images[0], predictions=prediction_groups[0], ax=axs )
+
+        columnbelow[0].pyplot(fig)
+        print(images)
+
+if uploaded_img is not None:
+    btn=st.button("Recognize Text", on_click=on_click)
+
+belowrecognize=st.empty()
+columnbelow=belowrecognize.columns(1) #Returns list
+
+
+
+
+
+