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import streamlit as st
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import matplotlib.pyplot as plt
import time
import os
import os
# Use a custom cache directory for Hugging Face models
os.environ["HF_HOME"] = "./hf_cache"
# Ensure directory exists and is writable
os.makedirs("./hf_cache", exist_ok=True)
model_name = "tabularisai/multilingual-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Function for sentiment prediction with progress bar
def predict_sentiment(texts):
sentiments = []
sentiment_map = {0: "Very Negative", 1: "Negative", 2: "Neutral", 3: "Positive", 4: "Very Positive"}
progress_bar = st.progress(0)
total_texts = len(texts)
for i, text in enumerate(texts):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
sentiment = sentiment_map[torch.argmax(probabilities, dim=-1).item()]
sentiments.append(sentiment)
# Update progress bar
progress_bar.progress((i + 1) / total_texts)
time.sleep(0.1) # Optional: To better visualize progress
return sentiments
# Streamlit UI
st.title("Sentiment Analysis App")
st.write("Upload a CSV or Excel file containing text data for sentiment analysis.")
# File upload
# uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])
uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"], accept_multiple_files=False)
if uploaded_file is not None:
try:
# Read file
if uploaded_file.name.endswith(".csv"):
df = pd.read_csv(uploaded_file)
else:
df = pd.read_excel(uploaded_file)
st.write("Dataset Preview:")
st.dataframe(df.head())
# Select text column
text_column = st.selectbox("Select the text column for analysis", df.columns)
except Exception as e:
st.error(f"Error reading file: {e}")
if st.button("Analyze Sentiment"):
# Get text data
texts = df[text_column].astype(str).tolist()
# Predict sentiments with progress bar
sentiments = predict_sentiment(texts)
df["Sentiment"] = sentiments
# Display results
st.write("Sentiment Analysis Results:")
st.dataframe(df[[text_column, "Sentiment"]])
# Pie chart of sentiment distribution
st.write("Sentiment Distribution:")
sentiment_counts = df["Sentiment"].value_counts()
fig, ax = plt.subplots()
ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct="%1.1f%%", startangle=90)
ax.axis("equal")
st.pyplot(fig)
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