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| from fastapi import FastAPI, HTTPException, UploadFile, File | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from fastapi.responses import HTMLResponse | |
| from fastapi.staticfiles import StaticFiles | |
| from pydantic import BaseModel | |
| from setfit import AbsaModel | |
| import logging | |
| from typing import List, Dict, Any | |
| import uvicorn | |
| import os | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Initialize FastAPI app | |
| app = FastAPI(title="ABSA Web Application", description="Aspect-Based Sentiment Analysis using SetFit models") | |
| # Add CORS middleware | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| # Global variable to store the model | |
| absa_model = None | |
| class TextInput(BaseModel): | |
| text: str | |
| class ABSAResponse(BaseModel): | |
| text: str | |
| predictions: List[Dict[str, Any]] | |
| success: bool | |
| message: str | |
| async def load_model(): | |
| """Load the ABSA model on startup""" | |
| global absa_model | |
| try: | |
| logger.info("Loading ABSA models...") | |
| absa_model = AbsaModel.from_pretrained( | |
| "ronalhung/setfit-absa-restaurants-aspect", | |
| "ronalhung/setfit-absa-restaurants-polarity", | |
| ) | |
| logger.info("Models loaded successfully!") | |
| except Exception as e: | |
| logger.error(f"Failed to load models: {str(e)}") | |
| raise e | |
| async def startup_event(): | |
| """Load model when the application starts""" | |
| await load_model() | |
| async def get_home(): | |
| """Serve the main HTML page""" | |
| html_content = """ | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>ABSA - Aspect-Based Sentiment Analysis</title> | |
| <script src="https://cdn.tailwindcss.com"></script> | |
| <script src="https://unpkg.com/react@18/umd/react.development.js"></script> | |
| <script src="https://unpkg.com/react-dom@18/umd/react-dom.development.js"></script> | |
| <script src="https://unpkg.com/@babel/standalone/babel.min.js"></script> | |
| </head> | |
| <body class="bg-gray-50"> | |
| <div id="root"></div> | |
| <script type="text/babel"> | |
| const { useState, useRef } = React; | |
| const App = () => { | |
| const [text, setText] = useState(''); | |
| const [results, setResults] = useState(null); | |
| const [loading, setLoading] = useState(false); | |
| const [error, setError] = useState(''); | |
| const fileInputRef = useRef(null); | |
| const handleAnalyze = async () => { | |
| if (!text.trim()) { | |
| setError('Please enter some text to analyze'); | |
| return; | |
| } | |
| setLoading(true); | |
| setError(''); | |
| try { | |
| const response = await fetch('/analyze', { | |
| method: 'POST', | |
| headers: { | |
| 'Content-Type': 'application/json', | |
| }, | |
| body: JSON.stringify({ text: text.trim() }), | |
| }); | |
| const data = await response.json(); | |
| if (data.success) { | |
| setResults(data); | |
| } else { | |
| setError(data.message || 'Analysis failed'); | |
| } | |
| } catch (err) { | |
| setError('Failed to analyze text. Please try again.'); | |
| console.error('Error:', err); | |
| } finally { | |
| setLoading(false); | |
| } | |
| }; | |
| const handleFileUpload = async (event) => { | |
| const file = event.target.files[0]; | |
| if (!file) return; | |
| if (!file.name.endsWith('.txt')) { | |
| setError('Please upload a .txt file'); | |
| return; | |
| } | |
| try { | |
| const text = await file.text(); | |
| setText(text); | |
| setError(''); | |
| } catch (err) { | |
| setError('Failed to read file. Please try again.'); | |
| console.error('Error reading file:', err); | |
| } | |
| }; | |
| const clearResults = () => { | |
| setText(''); | |
| setResults(null); | |
| setError(''); | |
| }; | |
| const getSentimentColor = (polarity) => { | |
| switch (polarity) { | |
| case 'positive': return 'text-green-600 bg-green-100'; | |
| case 'negative': return 'text-red-600 bg-red-100'; | |
| case 'neutral': return 'text-gray-600 bg-gray-100'; | |
| case 'conflict': return 'text-yellow-600 bg-yellow-100'; | |
| default: return 'text-gray-600 bg-gray-100'; | |
| } | |
| }; | |
| return ( | |
| <div className="min-h-screen bg-gradient-to-br from-blue-50 to-indigo-100"> | |
| <div className="container mx-auto px-4 py-8"> | |
| <div className="max-w-4xl mx-auto"> | |
| {/* Header */} | |
| <div className="text-center mb-8"> | |
| <h1 className="text-4xl font-bold text-gray-800 mb-4"> | |
| Aspect-Based Sentiment Analysis | |
| </h1> | |
| <p className="text-lg text-gray-600"> | |
| Analyze aspects and sentiments in restaurant reviews using SetFit models | |
| </p> | |
| </div> | |
| {/* Input Section */} | |
| <div className="bg-white rounded-lg shadow-lg p-6 mb-6"> | |
| <h2 className="text-2xl font-semibold text-gray-800 mb-4">Input Text</h2> | |
| {/* File Upload */} | |
| <div className="mb-4"> | |
| <label className="block text-sm font-medium text-gray-700 mb-2"> | |
| Upload Text File (.txt) | |
| </label> | |
| <input | |
| ref={fileInputRef} | |
| type="file" | |
| accept=".txt" | |
| onChange={handleFileUpload} | |
| className="block w-full text-sm text-gray-500 | |
| file:mr-4 file:py-2 file:px-4 | |
| file:rounded-md file:border-0 | |
| file:text-sm file:font-semibold | |
| file:bg-blue-50 file:text-blue-700 | |
| hover:file:bg-blue-100 | |
| cursor-pointer" | |
| /> | |
| </div> | |
| {/* Text Area */} | |
| <div className="mb-4"> | |
| <label className="block text-sm font-medium text-gray-700 mb-2"> | |
| Or type/paste your text here: | |
| </label> | |
| <textarea | |
| value={text} | |
| onChange={(e) => setText(e.target.value)} | |
| placeholder="Enter restaurant review text for analysis..." | |
| className="w-full h-32 p-3 border border-gray-300 rounded-md focus:ring-2 focus:ring-blue-500 focus:border-blue-500 resize-none" | |
| /> | |
| </div> | |
| {/* Error Message */} | |
| {error && ( | |
| <div className="mb-4 p-3 bg-red-100 border border-red-400 text-red-700 rounded-md"> | |
| {error} | |
| </div> | |
| )} | |
| {/* Action Buttons */} | |
| <div className="flex gap-3"> | |
| <button | |
| onClick={handleAnalyze} | |
| disabled={loading || !text.trim()} | |
| className="px-6 py-2 bg-blue-600 text-white rounded-md hover:bg-blue-700 | |
| disabled:bg-gray-400 disabled:cursor-not-allowed | |
| flex items-center gap-2 font-medium transition-colors" | |
| > | |
| {loading ? ( | |
| <> | |
| <div className="animate-spin rounded-full h-4 w-4 border-b-2 border-white"></div> | |
| Analyzing... | |
| </> | |
| ) : ( | |
| 'Analyze Text' | |
| )} | |
| </button> | |
| <button | |
| onClick={clearResults} | |
| className="px-6 py-2 bg-gray-500 text-white rounded-md hover:bg-gray-600 | |
| font-medium transition-colors" | |
| > | |
| Clear | |
| </button> | |
| </div> | |
| </div> | |
| {/* Results Section */} | |
| {results && ( | |
| <div className="bg-white rounded-lg shadow-lg p-6"> | |
| <h2 className="text-2xl font-semibold text-gray-800 mb-4">Analysis Results</h2> | |
| {/* Original Text */} | |
| <div className="mb-6"> | |
| <h3 className="text-lg font-medium text-gray-700 mb-2">Original Text:</h3> | |
| <div className="p-3 bg-gray-50 rounded-md border"> | |
| {results.text} | |
| </div> | |
| </div> | |
| {/* Predictions */} | |
| <div> | |
| <h3 className="text-lg font-medium text-gray-700 mb-4"> | |
| Detected Aspects & Sentiments: | |
| </h3> | |
| {results.predictions && results.predictions.length > 0 ? ( | |
| <div className="space-y-3"> | |
| {results.predictions.map((prediction, index) => ( | |
| <div key={index} className="border border-gray-200 rounded-md p-4"> | |
| <div className="flex items-center justify-between mb-2"> | |
| <span className="text-sm font-medium text-gray-600"> | |
| Aspect Span: | |
| </span> | |
| <span className="font-semibold text-gray-800"> | |
| "{prediction.span}" | |
| </span> | |
| </div> | |
| <div className="flex items-center justify-between"> | |
| <span className="text-sm font-medium text-gray-600"> | |
| Sentiment: | |
| </span> | |
| <span className={`px-3 py-1 rounded-full text-sm font-medium ${getSentimentColor(prediction.polarity)}`}> | |
| {prediction.polarity} | |
| </span> | |
| </div> | |
| </div> | |
| ))} | |
| </div> | |
| ) : ( | |
| <div className="text-gray-500 text-center py-4"> | |
| No aspects detected in the text. | |
| </div> | |
| )} | |
| </div> | |
| </div> | |
| )} | |
| </div> | |
| </div> | |
| </div> | |
| ); | |
| }; | |
| ReactDOM.render(<App />, document.getElementById('root')); | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| return html_content | |
| async def analyze_text(input_data: TextInput): | |
| """Analyze text for aspects and sentiment""" | |
| global absa_model | |
| if absa_model is None: | |
| raise HTTPException(status_code=503, detail="Model not loaded yet. Please try again later.") | |
| try: | |
| text = input_data.text.strip() | |
| if not text: | |
| return ABSAResponse( | |
| text=text, | |
| predictions=[], | |
| success=False, | |
| message="Empty text provided" | |
| ) | |
| logger.info(f"Analyzing text: {text[:100]}...") | |
| # Run ABSA analysis | |
| predictions = absa_model(text) | |
| # Format predictions for response | |
| formatted_predictions = [] | |
| if predictions: | |
| for pred in predictions: | |
| formatted_predictions.append({ | |
| "span": pred.get("span", ""), | |
| "polarity": pred.get("polarity", "neutral") | |
| }) | |
| return ABSAResponse( | |
| text=text, | |
| predictions=formatted_predictions, | |
| success=True, | |
| message="Analysis completed successfully" | |
| ) | |
| except Exception as e: | |
| logger.error(f"Error during analysis: {str(e)}") | |
| return ABSAResponse( | |
| text=input_data.text, | |
| predictions=[], | |
| success=False, | |
| message=f"Analysis failed: {str(e)}" | |
| ) | |
| async def health_check(): | |
| """Health check endpoint""" | |
| return { | |
| "status": "healthy", | |
| "model_loaded": absa_model is not None, | |
| "message": "ABSA service is running" | |
| } | |
| if __name__ == "__main__": | |
| uvicorn.run( | |
| "app:app", | |
| host="0.0.0.0", | |
| port=8000, | |
| reload=True, | |
| log_level="info" | |
| ) |