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import gradio as gr
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load model and tokenizer globally for efficiency
model_name = "tabularisai/multilingual-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict_sentiment(texts):
"""
Predict sentiment for a list of 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()]
def process_file(file_obj):
"""
Process the input file and add sentiment analysis results
"""
try:
# Read the file based on its extension
file_path = file_obj.name
if file_path.endswith('.csv'):
df = pd.read_csv(file_path)
elif file_path.endswith(('.xlsx', '.xls')):
df = pd.read_excel(file_path)
else:
raise ValueError("Unsupported file format. Please upload a CSV or Excel file.")
# Verify that 'Reviews' column exists
if 'Reviews' not in df.columns:
raise ValueError("Input file must contain a 'Reviews' column.")
# Perform sentiment analysis
reviews = df['Reviews'].fillna("") # Handle any missing values
sentiments = predict_sentiment(reviews.tolist())
# Add results to the dataframe
df['Sentiment'] = sentiments
# Save the results to a new Excel file
output_path = "output_with_sentiment.xlsx"
df.to_excel(output_path, index=False)
return df, output_path
except Exception as e:
raise gr.Error(str(e))
# Create Gradio interface
with gr.Blocks() as interface:
gr.Markdown("# Review Sentiment Analysis")
gr.Markdown("Upload an Excel or CSV file with a 'Reviews' column to analyze sentiment.")
with gr.Row():
file_input = gr.File(
label="Upload File (CSV or Excel)",
file_types=[".csv", ".xlsx", ".xls"]
)
with gr.Row():
analyze_btn = gr.Button("Analyze Sentiments")
with gr.Row():
output_df = gr.Dataframe(label="Results Preview")
output_file = gr.File(label="Download Results")
analyze_btn.click(
fn=process_file,
inputs=[file_input],
outputs=[output_df, output_file]
)
# Launch the interface
interface.launch() |