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import gradio as gr
import os
import sys
# Add current directory to path
current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, current_dir)
# Try to import advanced model, fallback to basic if needed
try:
from app.model import predict
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
# Try to load your custom model first
try:
MODEL_NAME = "fitsblb/YelpReviewsAnalyzer"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
CUSTOM_MODEL_AVAILABLE = True
except:
# Fallback to a general model
sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
CUSTOM_MODEL_AVAILABLE = False
except ImportError:
# Ultimate fallback
from transformers import pipeline
sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
CUSTOM_MODEL_AVAILABLE = False
def analyze_sentiment(text):
"""Analyze sentiment using available models"""
if not text.strip():
return "Please enter some text to analyze!"
try:
# Use the pipeline
result = sentiment_pipeline(text)
if isinstance(result, list) and len(result) > 0:
result = result[0]
sentiment = result['label']
confidence = result['score']
# Map labels to consistent format
if sentiment.upper() in ['POSITIVE', 'POS']:
sentiment = "Positive"
elif sentiment.upper() in ['NEGATIVE', 'NEG']:
sentiment = "Negative"
elif sentiment.upper() in ['NEUTRAL', 'NEU']:
sentiment = "Neutral"
model_info = "YelpReviewsAnalyzer (Custom)" if CUSTOM_MODEL_AVAILABLE else "RoBERTa (Fallback)"
output = f"""
## 🎯 Sentiment Analysis Result
**Sentiment**: {sentiment}
**Confidence**: {confidence:.3f}
**Model**: {model_info}
---
*Analyzing sentiment with AI models*
"""
return output
except Exception as e:
return f"❌ Error analyzing sentiment: {str(e)}"
# Create Gradio interface
demo = gr.Interface(
fn=analyze_sentiment,
inputs=gr.Textbox(
label="📝 Enter Text for Sentiment Analysis",
placeholder="Type your text here... (e.g., 'This restaurant has amazing food!')",
lines=3
),
outputs=gr.Markdown(label="🎯 Analysis Results"),
title="🚀 Sentiment Analyzer",
description="""
**AI-Powered Sentiment Analysis**
This system analyzes the sentiment of your text using transformer models.
Enter any text and get instant sentiment predictions with confidence scores!
""",
examples=[
["This restaurant has absolutely amazing food and incredible service!"],
["The food was terrible and the service was slow."],
["It's an okay place, nothing special but not bad either."],
["I love this product! Best purchase I've ever made."],
["This movie was boring and way too long."]
],
theme=gr.themes.Soft(),
allow_flagging="never"
)
if __name__ == "__main__":
demo.launch()
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