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import streamlit as st
from utils.util_classifier import TextClassificationPipeline
import time
import requests
import io
import pdfplumber
from urllib.parse import urlparse
import plotly.graph_objects as go
import plotly.express as px
def validate_url(url):
try:
result = urlparse(url)
return all([result.scheme, result.netloc])
except:
return False
def download_pdf(url):
try:
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
'Accept': 'application/pdf,*/*',
'Referer': 'https://www.inter-lux.com/'
}
response = requests.get(url, headers=headers)
response.raise_for_status()
# Verify content type is PDF
content_type = response.headers.get('content-type', '')
if 'application/pdf' not in content_type.lower():
raise ValueError(f"URL does not point to a PDF file. Content-Type: {content_type}")
return io.BytesIO(response.content)
except Exception as e:
st.error(f"Download error: {str(e)}")
return None
def extract_text(pdf_file):
try:
# Reset file pointer
pdf_file.seek(0)
with pdfplumber.open(pdf_file) as pdf:
text = ""
for page in pdf.pages:
extracted = page.extract_text()
if extracted:
text += extracted + "\n"
if not text.strip():
raise ValueError("No text could be extracted from the PDF")
return text.strip()
except Exception as e:
st.error(f"Text extraction error: {str(e)}")
return None
def main():
st.title("🎯 Document Classifier")
# Model selection
method = "bertbased"
# Initialize classifier
classifier = TextClassificationPipeline(method=method)
# File input tabs
tab1, tab2 = st.tabs(["🔗 URL Input", "📁 File Upload"])
with tab1:
url = st.text_input("Enter PDF URL")
process_btn = st.button("Classify Document", key="url_classify")
if process_btn and url:
if not validate_url(url):
st.error("Please enter a valid URL")
return
progress_container = st.container()
with progress_container:
# Step 1: Downloading
with st.spinner("Downloading PDF..."):
pdf_file = download_pdf(url)
if pdf_file is None:
return
st.success("PDF downloaded successfully!")
# Step 2: Extracting Text
with st.spinner("Extracting text from PDF..."):
text = extract_text(pdf_file)
if text is None or len(text.strip()) == 0:
return
st.success("Text extracted successfully!")
with st.expander("View Extracted Text"):
st.text(text[:500] + "..." if len(text) > 500 else text)
# Step 3: Classification
with st.spinner("Classifying document..."):
result = classifier.predict(text, return_probability=True)
if isinstance(result, list):
result = result[0]
# Display results
def create_gauge_chart(confidence):
"""Create a gauge chart for confidence score"""
fig = go.Figure(go.Indicator(
mode = "gauge+number+delta",
value = confidence * 100,
domain = {'x': [0, 1], 'y': [0, 1]},
gauge = {
'axis': {'range': [None, 100], 'tickwidth': 1, 'tickcolor': "darkblue"},
'bar': {'color': "darkblue"},
'bgcolor': "white",
'borderwidth': 2,
'bordercolor': "gray",
'steps': [
{'range': [0, 50], 'color': '#FF9999'},
{'range': [50, 75], 'color': '#FFCC99'},
{'range': [75, 100], 'color': '#99FF99'}
],
},
title = {'text': "Confidence Score"}
))
fig.update_layout(
height=300,
margin=dict(l=10, r=10, t=50, b=10),
paper_bgcolor='rgba(0,0,0,0)',
font={'color': "darkblue", 'family': "Arial"}
)
return fig
def create_probability_chart(probabilities):
"""Create a horizontal bar chart for probability distribution"""
labels = list(probabilities.keys())
values = list(probabilities.values())
fig = go.Figure()
# Add bars
fig.add_trace(go.Bar(
y=labels,
x=[v * 100 for v in values],
orientation='h',
marker=dict(
color=[px.colors.sequential.Blues[i] for i in range(2, len(labels) + 2)],
line=dict(color='rgba(0,0,0,0.8)', width=2)
),
text=[f'{v:.1f}%' for v in [v * 100 for v in values]],
textposition='auto',
))
# Update layout
fig.update_layout(
title=dict(
text='Probability Distribution',
y=0.95,
x=0.5,
xanchor='center',
yanchor='top',
font=dict(size=20, color='darkblue')
),
xaxis_title="Probability (%)",
yaxis_title="Categories",
height=400,
margin=dict(l=20, r=20, t=70, b=20),
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
font=dict(family="Arial", size=14),
showlegend=False
)
# Update axes
fig.update_xaxes(
range=[0, 100],
gridcolor='rgba(0,0,0,0.1)',
zerolinecolor='rgba(0,0,0,0.2)'
)
fig.update_yaxes(
gridcolor='rgba(0,0,0,0.1)',
zerolinecolor='rgba(0,0,0,0.2)'
)
return fig
# Update the results display section
def display_results(result):
"""Display classification results with modern visualizations"""
# Create three columns for the results
col1, col2 = st.columns([1, 2])
with col1:
# Predicted Category Card
st.markdown("""
<div style='
background-color: white;
padding: 20px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
text-align: center;
margin-bottom: 20px;
'>
<h4 style='color: #1f77b4; margin-bottom: 10px;'>Predicted Category</h4>
<p style='
font-size: 24px;
font-weight: bold;
color: #2c3e50;
margin: 0;
padding: 10px;
background-color: #f8f9fa;
border-radius: 5px;
'>{}</p>
</div>
""".format(result['predicted_label']), unsafe_allow_html=True)
# Confidence Gauge
st.plotly_chart(create_gauge_chart(result['confidence']), use_container_width=True)
with col2:
# Probability Distribution
st.plotly_chart(create_probability_chart(result['probabilities']), use_container_width=True)
# Add metadata section
with st.expander("📊 Classification Details"):
st.markdown(f"""
- **Model Type**: {result['model_type'].title()}
- **Document Length**: {len(result['text'])} characters
""")
# Update the main classification results section
# Replace the existing results display with:
st.markdown("### 📊 Classification Results")
display_results(result)
with tab2:
uploaded_file = st.file_uploader("Upload PDF file", type="pdf")
process_btn = st.button("Classify Document", key="file_classify")
if process_btn and uploaded_file:
with st.spinner("Processing uploaded PDF..."):
text = extract_text(uploaded_file)
if text is None:
return
result = classifier.predict(text, return_probability=True)
if isinstance(result, list):
result = result[0]
# Display results (same as URL tab)
st.markdown("### 📊 Classification Results")
confidence = result['confidence']
st.markdown(f"""
<div class="confidence-meter">
<div class="meter-fill" style="width: {confidence*100}%"></div>
<span class="meter-text">{confidence:.1%} Confident</span>
</div>
""", unsafe_allow_html=True)
st.markdown(f"""
<div class="result-card">
<h4>Predicted Category</h4>
<p class="prediction">{result['predicted_label']}</p>
</div>
""", unsafe_allow_html=True)
st.markdown("#### Probability Distribution")
for label, prob in result['probabilities'].items():
st.markdown(f"""
<div class="prob-bar">
<span class="label">{label}</span>
<div class="bar">
<div class="fill" style="width: {prob*100}%"></div>
</div>
<span class="value">{prob:.1%}</span>
</div>
""", unsafe_allow_html=True)
main() |