Update app.py

app.py

@@ -5,6 +5,109 @@ from streamlit_cropper import st_cropper
 from annotated_text import annotated_text
 from simpletransformers.ner import NERModel
 import pytesseract
+import os
+def process_image(img_file):
+    st.write("Crop ส่วนที่ต้องการ (คุณลักษณะ)")
+    aspect_ratio = None
+
+    col1, col2 = st.sidebar.columns(2)
+    with col1:
+        box_color = st.color_picker(label="Box Color", value='#0000FF')
+    return_type = "image"
+    realtime_update = st.sidebar.checkbox(label="Update in Real Time", value=True)
+
+    img = Image.open(img_file)
+    width, height = img.size
+
+    if not realtime_update:
+        st.write("Double click to save crop")
+
+    if return_type == 'box':
+        rect = st_cropper(
+            img,
+            realtime_update=realtime_update,
+            box_color=box_color,
+            aspect_ratio=aspect_ratio,
+            return_type=return_type
+        )
+        raw_image = np.asarray(img).astype('uint8')
+        left, top, width, height = tuple(map(int, rect.values()))
+        st.write(rect)
+        masked_image = np.zeros(raw_image.shape, dtype='uint8')
+        masked_image[top:top + height, left:left + width] = raw_image[top:top + height, left:left + width]
+        st.image(Image.fromarray(masked_image))
+    else:
+        # Get a cropped image from the frontend
+        cropped_img = st_cropper(
+            img,
+            realtime_update=realtime_update,
+            box_color=box_color,
+            aspect_ratio=aspect_ratio,
+            return_type=return_type
+        )
+
+        # Manipulate cropped image at will
+        st.write("Preview")
+        _ = cropped_img.thumbnail((1000, 1000))
+
+        desired_dpi = 300
+
+        # Resize the image
+        resized_image = resize_image(cropped_img, desired_dpi)
+
+        # Display the image
+        st.image(resized_image)
+        # Perform OCR
+        # extracted_text = 
+
+        # Display extracted text
+        txt_input = st.text_area("โปรดตรวจสอบความถูกต้อง", perform_ocr(cropped_img))
+
+        word_output = ""
+        word_tuple = ()
+        check = st.button("Check", type="primary", use_container_width=True)
+        if check:
+            st.header("สรุปผล")
+            predictions, raw_outputs = model.predict([txt_input])
+            wrongword_lst = []
+
+            for i in predictions[0]:
+                word = i.values()
+                label = list(word)[0]
+                if label == "1":
+                    wrong_word = list(i.keys())[0]
+                    wrongword_lst.append(wrong_word)
+
+            word_tuple = ()
+            for i in predictions[0]:
+                label = i.values()
+                word = list(i.keys())[0]
+                label = list(label)[0]
+                if label == "1":
+                    word_tuple = (*word_tuple, (f'{word} ', 'มีการล็อคสเปค', '#F41B15'))
+                else:
+                    word_tuple = (*word_tuple, f'{word} ')
+            word_output = wrongword_lst
+
+        if word_output != "":
+            annotated_text(list(word_tuple))
+
+            col1, col2 = st.columns(2)
+            with col1:
+                st.header("คำที่สุ่มเสี่ยง")
+                st.write(word_output)
+
+
+
+
+
+
+def get_sample_images(folder):
+    sample_images = []
+    for filename in os.listdir(folder):
+        if filename.endswith(".jpg") or filename.endswith(".png"):
+            sample_images.append(os.path.join(folder, filename))
+    return sample_images
 # Function to resize the image based on desired DPI
 def resize_image(image, desired_dpi):
     # Calculate the current DPI
@@ -37,97 +140,23 @@ def main():
  
     st.set_option('deprecation.showfileUploaderEncoding', False)
 
-    img_file = st.sidebar.file_uploader(label='Upload a file', type=['png', 'jpg'])
-    if img_file:
-        st.write("Crop ส่วนที่ต้องการ (คุณลักษณะ)")
-        aspect_ratio = None
-
-        col1, col2 = st.sidebar.columns(2)
-        with col1:
-            box_color = st.color_picker(label="Box Color", value='#0000FF')
-        return_type = "image"
-        realtime_update = st.sidebar.checkbox(label="Update in Real Time", value=True)
-
-        img = Image.open(img_file)
-        width, height = img.size
-
-        if not realtime_update:
-            st.write("Double click to save crop")
-
-        if return_type == 'box':
-            rect = st_cropper(
-                img,
-                realtime_update=realtime_update,
-                box_color=box_color,
-                aspect_ratio=aspect_ratio,
-                return_type=return_type
-            )
-            raw_image = np.asarray(img).astype('uint8')
-            left, top, width, height = tuple(map(int, rect.values()))
-            st.write(rect)
-            masked_image = np.zeros(raw_image.shape, dtype='uint8')
-            masked_image[top:top + height, left:left + width] = raw_image[top:top + height, left:left + width]
-            st.image(Image.fromarray(masked_image))
-        else:
-            # Get a cropped image from the frontend
-            cropped_img = st_cropper(
-                img,
-                realtime_update=realtime_update,
-                box_color=box_color,
-                aspect_ratio=aspect_ratio,
-                return_type=return_type
-            )
-
-            # Manipulate cropped image at will
-            st.write("Preview")
-            _ = cropped_img.thumbnail((1000, 1000))
-
-            desired_dpi = 300
-
-            # # Resize the image
-            resized_image = resize_image(cropped_img, desired_dpi)
-
-            # # Display the image
-            st.image(resized_image)
-            # Perform OCR
-            # extracted_text = 
-
-            # Display extracted text
-            txt_input = st.text_area("โปรดตรวจสอบความถูกต้อง", perform_ocr(cropped_img))
-
-            word_output = ""
-            word_tuple = ()
-            check = st.button("ตรวจสอบ",type="primary",use_container_width=True)
-            if check:
-                st.header("สรุปผล")
-                predictions, raw_outputs = model.predict([txt_input])
-                wrongword_lst = []
-
-                for i in predictions[0]:
-                    word = i.values()
-                    label = list(word)[0]
-                    if label == "1":
-                        wrong_word = list(i.keys())[0]
-                        wrongword_lst.append(wrong_word)
-
-                word_tuple = ()
-                for i in predictions[0]:
-                    label = i.values()
-                    word = list(i.keys())[0]
-                    label = list(label)[0]
-                    if label == "1":
-                        word_tuple = (*word_tuple, (f'{word} ', 'มีการล็อคสเปค', '#F41B15'))
-                    else:
-                        word_tuple = (*word_tuple, f'{word} ')
-                word_output = wrongword_lst
-
-            if word_output != "":
-                annotated_text(list(word_tuple))
-
-                col1, col2 = st.columns(2)
-                with col1:
-                    st.header("คำที่สุ่มเสี่ยง")
-                    st.write(word_output)
+    img_files = st.sidebar.file_uploader(label='Upload a file', type=['png', 'jpg'], accept_multiple_files=False)
+    sample_folder = "test_img"
+    sample_images = get_sample_images(sample_folder)
+    
+    # Display the sample images in the sidebar
+    selection = st.sidebar.selectbox("Select a sample", ["Browse files Mode"] + sample_images, format_func=lambda x: os.path.basename(x))
+    if selection and selection != "Browse files Mode":
+        img_file = selection
+        process_image(img_file)
+        
+        
+        
+
+    elif img_files:
+        img_file = img_files
+        process_image(img_file)
+        
     
 
 if __name__ == '__main__':
@@ -144,15 +173,21 @@ if __name__ == '__main__':
         Hardware_Type = False
     # Model initialization
     _NER_TAGS = ['0', '1']
-    try:
-        model = NERModel(
-            model_name=r"model",
-            model_type="camembert",
-            labels=_NER_TAGS,
-            use_cuda=Hardware_Type
-        )
-        main()
-    except:
-        st.warning("Cuda not available, Please checking your hardware")
+    # try:
+    model = NERModel(
+        model_name=r"model",
+        model_type="camembert",
+        labels=_NER_TAGS,
+        use_cuda=Hardware_Type
+    )
+    
+    # except:
+    #     st.warning("Cuda not available, Please checking your hardware")
+        # Get the sample images
+
+    
+    main()
+    # Display the selected image
+
     aib_logo = Image.open(r'aib_logo.png')
     add_logo = st.sidebar.image(aib_logo)