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import streamlit as st
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import matplotlib.pyplot as plt
# Load model and tokenizer
model_name = "tabularisai/multilingual-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict_sentiment(texts):
inputs = tokenizer(texts, 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_map = {0: "Very Negative", 1: "Negative", 2: "Neutral", 3: "Positive", 4: "Very Positive"}
return [sentiment_map[p] for p in torch.argmax(probabilities, dim=-1).tolist()]
# Streamlit UI
st.title("Sentiment Analysis App")
st.write("Upload an Excel file containing text data, and we'll analyze its sentiment.")
uploaded_file = st.file_uploader("Upload Excel File", type=["xlsx", "xls"])
if uploaded_file is not None:
df = pd.read_excel(uploaded_file)
st.write("Preview of Uploaded Data:")
st.dataframe(df.head())
text_column = st.selectbox("Select the column containing text", df.columns)
if st.button("Analyze Sentiment"):
df["Sentiment"] = predict_sentiment(df[text_column].astype(str).tolist())
# Display results
st.write("Sentiment Analysis Results:")
st.dataframe(df[[text_column, "Sentiment"]])
# Pie chart
sentiment_counts = df["Sentiment"].value_counts()
fig, ax = plt.subplots()
ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', colors=["red", "yellow", "pink", "lightgreen", "green"])
ax.set_title("Sentiment Distribution")
st.pyplot(fig)
# Table display for sentiment analysis results
st.write("Detailed Sentiment Table:")
st.table(df[[text_column, "Sentiment"]])
# Download option
st.download_button("Download Results", df.to_csv(index=False).encode('utf-8'), "sentiment_results.csv", "text/csv") |