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import streamlit as st
import pandas as pd
import time
import plotly.express as px
from utils.file_handler import validate_file
from models.sentiment_model import load_model
# Constants
MAX_FILE_SIZE_MB = 200
# Load sentiment analysis pipeline
sentiment_pipeline = load_model()
st.title("📊 Sentiment Analysis App")
# File upload
uploaded_file = st.file_uploader("Upload a CSV file (Max: 200MB)", type=["csv"])
if uploaded_file is not None:
if validate_file(uploaded_file, MAX_FILE_SIZE_MB):
df = pd.read_csv(uploaded_file)
# Check for 'text' column or ask user for correct column
if "text" not in df.columns:
text_column = st.selectbox("Select the column containing text values", df.columns)
else:
text_column = "text"
if st.button("Analyze Sentiment"):
st.write("Processing sentiment analysis...")
progress_bar = st.progress(0)
sentiments = []
for i, text in enumerate(df[text_column].dropna()):
result = sentiment_pipeline(text)
sentiments.append(result[0]["label"])
progress_bar.progress((i + 1) / len(df))
time.sleep(0.1)
df["Sentiment"] = sentiments
# Display results
st.write("Sentiment Analysis Results:")
st.dataframe(df[[text_column, "Sentiment"]])
# Create pie chart
sentiment_counts = df["Sentiment"].value_counts().reset_index()
sentiment_counts.columns = ["Sentiment", "Count"]
fig = px.pie(sentiment_counts, names="Sentiment", values="Count", title="Sentiment Distribution")
st.plotly_chart(fig)
# Allow CSV download
# st.download_button("Download Results", df.to_csv(index=False), "sentiment_results.csv", "text/csv")
# Download option
st.download_button("Download Results", df.to_csv(index=False).encode('utf-8'), "sentiment_results.csv", "text/csv")
else:
st.error("File exceeds the maximum allowed size of 200MB. Please upload a smaller file.")
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