Update app.py

app.py

@@ -1,161 +1,162 @@
-import os
-import requests
-import streamlit as st
-import streamlit.components.v1 as components
-from streamlit_extras.add_vertical_space import add_vertical_space
-from bs4 import BeautifulSoup
-from dotenv import load_dotenv
-from warnings import filterwarnings
-filterwarnings('ignore')
-
-
-def streamlit_config():
-
-    # page configuration
-    st.set_page_config(page_title='Document Classification', layout='centered')
-
-    # page header transparent color
-    page_background_color = """
-    <style>
-
-    [data-testid="stHeader"] 
-    {
-    background: rgba(0,0,0,0);
-    }
-
-    </style>
-    """
-    st.markdown(page_background_color, unsafe_allow_html=True)
-
-    # title and position
-    st.markdown(f'<h1 style="text-align: center;">Financial Document Classification</h1>',
-                unsafe_allow_html=True)
-    add_vertical_space(2)
-
-
-def display_html_document(input_file):
-
-    # Read the file content
-    html_content = input_file.getvalue().decode("utf-8")
-
-    # Define CSS to control the container size and center content
-    styled_html = f"""
-    <div style="width: 610px; height: 300px; 
-                overflow: auto; border: 1px solid #ddd; 
-                padding: 10px; background-color: white; 
-                color: black; white-space: normal; 
-                display: block;">
-        {html_content}
-    </div>
-    """
-
-    # Display the HTML content inside a fixed-size container
-    components.html(styled_html, height=320, width=650, scrolling=False)
-
-
-def text_extract_from_html(html_file):
-
-    # Read the uploaded HTML file
-    html_content = html_file.read().decode('utf-8')
-
-    # Parse the HTML Content
-    soup = BeautifulSoup(html_content, 'html.parser')
-
-    # Extract the Text
-    text = soup.get_text()
-
-    # Split the Text and Remove Unwanted Space
-    result = [i.strip() for i in text.split()]
-    result = ' '.join(result)
-
-    return result
-
-
-def classify_text_with_huggingface_api(extracted_text):
-     
-    # Load environment variables from .env file
-    load_dotenv()
-
-    # Retrieve the Hugging Face API token from environment variables
-    hf_token = os.getenv("HUGGINGFACE_TOKEN")
-
-    # Define the Hugging Face API URL for the model
-    API_URL = "https://api-inference.huggingface.co/models/gopiashokan/Financial-Document-Classification-using-Deep-Learning"
-
-    # Set the authorization headers with the Hugging Face token
-    HEADERS = {"Authorization": f"Bearer {hf_token}"}
-
-    # Send a POST request to the Hugging Face API with the extracted text
-    response = requests.post(API_URL, headers=HEADERS, json={"inputs": extracted_text})
-    
-    # Parse and return the JSON response
-    if response.status_code == 200:
-        result = response.json()
-        return result[0]
-    
-    else:
-        return None
-
-    
-def prediction(input_file):
-
-    # Extract text from the uploaded HTML file  
-    extracted_text = text_extract_from_html(input_file)
-
-    # Limit the extracted text to the first 512 characters to avoid API input limits  
-    extracted_text = extracted_text[0:512]
-
-    # Classify the extracted text using the Hugging Face API  
-    result = classify_text_with_huggingface_api(extracted_text)
-
-    if result is not None:
-        # Select the prediction with the highest confidence score  
-        prediction = max(result, key=lambda x: x['score'])
-
-        # Map model labels to human-readable class names 
-        label_mapping = {'LABEL_0':'Others', 'LABEL_1':'Balance Sheets', 'LABEL_2':'Notes', 'LABEL_3':'Cash Flow', 'LABEL_4':'Income Statement'}
-
-        # Get the predicted class name based on the model output  
-        predicted_class = label_mapping[prediction['label']]
-
-        # Convert the confidence score to a percentage  
-        confidence = prediction['score'] * 100
-
-        # Display the prediction results
-        add_vertical_space(1)
-        st.markdown(f"""
-            <div style="text-align: center; line-height: 1; padding: 0px;">
-                <h4 style="color: orange; margin: 0px; padding: 0px;">{confidence:.2f}% Match Found</h4>
-                <h3 style="color: green; margin-top: 10px; padding: 0px;">Predicted Class = {predicted_class}</h3>
-            </div>
-        """, unsafe_allow_html=True)
-
-
-    else:
-        add_vertical_space(1)
-        st.markdown(f'<h4 style="text-align: center; color: orange; margin-top: 10px;">Refresh the Page and Try Again</h4>', 
-                        unsafe_allow_html=True)
-
-
-
-# Streamlit Configuration Setup
-streamlit_config()
-    
-
-try:
-
-    # File uploader to upload the HTML file
-    input_file = st.file_uploader('Upload an HTML file', type='html')
-
-    if input_file is not None:
-        
-        # Display the HTML Document to User Interface
-        display_html_document(input_file)
-        
-        # Predict the Class and Confidence Score
-        with st.spinner('Processing'):
-            prediction(input_file)
-
-
-except Exception as e:
-    st.markdown(f'<h3 style="text-align: center;">{e}</h3>', unsafe_allow_html=True)
+import os
+import requests
+import streamlit as st
+import streamlit.components.v1 as components
+from streamlit_extras.add_vertical_space import add_vertical_space
+from bs4 import BeautifulSoup
+from dotenv import load_dotenv
+from warnings import filterwarnings
+filterwarnings('ignore')
+
+
+def streamlit_config():
+
+    # page configuration
+    st.set_page_config(page_title='Document Classification', layout='centered')
+
+    # page header transparent color
+    page_background_color = """
+    <style>
+
+    [data-testid="stHeader"] 
+    {
+    background: rgba(0,0,0,0);
+    }
+
+    </style>
+    """
+    st.markdown(page_background_color, unsafe_allow_html=True)
+
+    # title and position
+    st.markdown(f'<h1 style="text-align: center;">Financial Document Classification</h1>',
+                unsafe_allow_html=True)
+    add_vertical_space(2)
+
+
+def display_html_document(input_file):
+
+    # Read the file content
+    html_content = input_file.getvalue().decode("utf-8")
+
+    # Define CSS to control the container size and center content
+    styled_html = f"""
+    <div style="width: 610px; height: 300px; 
+                overflow: auto; border: 1px solid #ddd; 
+                padding: 10px; background-color: white; 
+                color: black; white-space: normal; 
+                display: block;">
+        {html_content}
+    </div>
+    """
+
+    # Display the HTML content inside a fixed-size container
+    components.html(styled_html, height=320, width=650, scrolling=False)
+
+
+def text_extract_from_html(html_file):
+
+    # Read the uploaded HTML file
+    html_content = html_file.read().decode('utf-8')
+
+    # Parse the HTML Content
+    soup = BeautifulSoup(html_content, 'html.parser')
+
+    # Extract the Text
+    text = soup.get_text()
+
+    # Split the Text and Remove Unwanted Space
+    result = [i.strip() for i in text.split()]
+    result = ' '.join(result)
+
+    return result
+
+
+def classify_text_with_huggingface_api(extracted_text):
+     
+    # Load environment variables from .env file
+    load_dotenv()
+
+    # Retrieve the Hugging Face API token from environment variables
+    hf_token = os.getenv("HUGGINGFACE_TOKEN")
+
+    # Define the Hugging Face API URL for the model
+    API_URL = "https://api-inference.huggingface.co/models/gopiashokan/Financial-Document-Classification-using-Deep-Learning"
+
+    # Set the authorization headers with the Hugging Face token
+    HEADERS = {"Authorization": f"Bearer {hf_token}"}
+
+    # Send a POST request to the Hugging Face API with the extracted text
+    response = requests.post(API_URL, headers=HEADERS, json={"inputs": extracted_text})
+    
+    # Parse and return the JSON response
+    if response.status_code == 200:
+        result = response.json()
+        return result[0]
+    
+    else:
+        return None
+
+    
+def prediction(input_file):
+
+    # Extract text from the uploaded HTML file  
+    extracted_text = text_extract_from_html(input_file)
+
+    # Limit the extracted text to the first 512 characters to avoid API input limits  
+    extracted_text = extracted_text[0:512]
+
+    # Classify the extracted text using the Hugging Face API  
+    result = classify_text_with_huggingface_api(extracted_text)
+
+    if result is not None:
+        # Select the prediction with the highest confidence score  
+        prediction = max(result, key=lambda x: x['score'])
+
+        # Map model labels to human-readable class names 
+        label_mapping = {'LABEL_0':'Others', 'LABEL_1':'Balance Sheets', 'LABEL_2':'Notes', 'LABEL_3':'Cash Flow', 'LABEL_4':'Income Statement'}
+
+        # Get the predicted class name based on the model output  
+        predicted_class = label_mapping[prediction['label']]
+
+        # Convert the confidence score to a percentage  
+        confidence = prediction['score'] * 100
+
+        # Display the prediction results
+        add_vertical_space(1)
+        st.markdown(f"""
+            <div style="text-align: center; line-height: 1; padding: 0px;">
+                <h4 style="color: orange; margin: 0px; padding: 0px;">{confidence:.2f}% Match Found</h4>
+                <h3 style="color: green; margin-top: 10px; padding: 0px;">Predicted Class = {predicted_class}</h3>
+            </div>
+        """, unsafe_allow_html=True)
+
+
+    else:
+        add_vertical_space(1)
+        st.markdown(f'<h4 style="text-align: center; color: orange; margin-top: 10px;">Refresh the Page and Try Again</h4>', 
+                        unsafe_allow_html=True)
+
+
+
+# Streamlit Configuration Setup
+streamlit_config()
+    
+
+try:
+
+    # File uploader to upload the HTML file
+    input_file = st.file_uploader('Upload an HTML file', type='html')
+
+    if input_file is not None:
+        
+        # Display the HTML Document to User Interface
+        display_html_document(input_file)
+        
+        # Predict the Class and Confidence Score
+        with st.spinner('Processing'):
+            prediction(input_file)
+            add_vertical_space(1)
+
+
+except Exception as e:
+    st.markdown(f'<h3 style="text-align: center;">{e}</h3>', unsafe_allow_html=True)