resolved bug of dataframe

app.py

@@ -72,7 +72,7 @@ if data:
     # Apply OCR and NER
     file_name = ocr(img_name)
     Output_dict = ner(file_name)
-    df = pd.DataFrame(Output_dict)
+    # df = pd.DataFrame(Output_dict)
 
     ocr_data = ""
     with open(os.path.join('runs', 'segment', path['MAIN_FLOW_INFERENCE_FOLDER'], 'ocr_label_data', data.name.split('.')[0]+'.txt'),'r+') as f :
@@ -81,6 +81,75 @@ if data:
     st.text(ocr_data)
 
     st.header("NER Output")
-    df = df.T
-    df.columns = ['Value']
-    st.table(df.T)
+    
+    new_df = pd.DataFrame()
+    new_df['Entity'] = list(Output_dict.keys())
+    
+    # print(df)
+    new_df['Value'] = list(Output_dict.values())
+    new_df['Value'] = new_df['Value'].astype('str')
+    st.table(new_df)
+
+else:
+    img_name = '3.jpg'
+    img = cv2.imread(img_name,0)
+    
+    if img.shape[0] > 1500:
+        height, width = img.shape 
+        img = img[height//4:-height//4, width//4:-width//4]
+    
+    cv2.imwrite(os.path.join('grey_images',img_name), img)
+
+    #call main function
+    # main(os.path.join('grey_images',img_name))
+    file_path = os.path.join('grey_images',img_name)
+    img_name = os.path.basename(file_path)
+
+
+    col1,col2 = st.columns(2)
+
+    with col1:
+        st.markdown("<h3 style='text-align: center;'>Grey Image</h1>", unsafe_allow_html=True) 
+        st.image(os.path.join('grey_images',img_name))
+
+        # Object detection and enhance image
+        seg_result, img_file = object_detection(file_path)
+        croped_img = crop_image(seg_result, img_file, img_name)
+        image = enhance_image(croped_img, img_name)
+
+        st.markdown("<h3 style='text-align: center;'>Enhanced Image</h1>", unsafe_allow_html=True) 
+        st.image(os.path.join('runs', 'segment', path['MAIN_FLOW_INFERENCE_FOLDER'], 'enhanced', img_name))
+
+    
+    with col2:
+        st.markdown("<h3 style='text-align: center;'>Detected Image</h1>", unsafe_allow_html=True) 
+        st.image(os.path.join('runs', 'segment',path['MAIN_FLOW_INFERENCE_FOLDER'],img_name))
+
+        # Rotation
+        processed_img = morphological_transform(image)
+        rotated_image, image = hoffman_transform(processed_img, image)
+        img_name = pytesseract_rotate(rotated_image, image, img_name)
+
+        st.markdown("<h3 style='text-align: center;'>Rotated Image</h1>", unsafe_allow_html=True) 
+        st.image(os.path.join('runs', 'segment', path['MAIN_FLOW_INFERENCE_FOLDER'], 'rotated_image', img_name))
+
+    # Apply OCR and NER
+    file_name = ocr(img_name)
+    Output_dict = ner(file_name)
+    # df = pd.DataFrame(Output_dict)
+
+    ocr_data = ""
+    with open(os.path.join('runs', 'segment', path['MAIN_FLOW_INFERENCE_FOLDER'], 'ocr_label_data', img_name.split('.')[0]+'.txt'),'r+') as f :
+        ocr_data = f.read()
+    st.header("OCR Text Output")
+    st.text(ocr_data)
+
+    st.header("NER Output")
+    
+    new_df = pd.DataFrame()
+    new_df['Entity'] = list(Output_dict.keys())
+    
+    # print(df)
+    new_df['Value'] = list(Output_dict.values())
+    new_df['Value'] = new_df['Value'].astype('str')
+    st.table(new_df)