import gradio as gr
import pandas as pd
import re
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
import string
import numpy as np
# Load data
df = pd.read_csv('car_rental_feedback_sentiment.csv - Copy (1).csv') # Make sure filename matches
def preprocess_text(text):
if not isinstance(text, str):
return ""
text = text.lower()
text = text.translate(str.maketrans('', '', string.punctuation))
text = re.sub('\s+', ' ', text).strip()
return text
# Check if required columns exist
if not all(col in df.columns for col in ['review', 'sentiment_value']):
raise ValueError("CSV file must contain 'review' and 'sentiment_value' columns")
df['cleaned_review'] = df['review'].apply(preprocess_text)
vectorizer = TfidfVectorizer(max_features=1000)
X = vectorizer.fit_transform(df['cleaned_review'])
y = df['sentiment_value']
model = LogisticRegression(max_iter=1000)
model.fit(X, y)
def predict_sentiment(text):
try:
cleaned_text = preprocess_text(text)
text_vector = vectorizer.transform([cleaned_text])
prediction = model.predict(text_vector)[0]
probabilities = model.predict_proba(text_vector)[0]
confidence = probabilities.max()
sentiment_label = 'Positive' if prediction == 1 else 'Negative' if prediction == -1 else 'Neutral'
return f"{sentiment_label} (confidence: {confidence*100:.1f}%)"
except Exception as e:
return f"Error processing your input: {str(e)}"
description = """
Car Rental Feedback Analyzer
Enter your car rental review below. The app will predict the sentiment (Positive, Neutral, Negative).
"""
iface = gr.Interface(
fn=predict_sentiment,
inputs=gr.Textbox(label="Enter car rental review", placeholder="Type your car rental experience here..."),
outputs=gr.Textbox(label="Sentiment Prediction"),
title="🚗 Car Rental Feedback Analyzer",
description=description,
examples=[
["The car was clean and the staff was friendly"],
["Terrible experience with late delivery"],
["Average service, nothing special"]
]
)
# Test locally in Colab first
iface.launch()
