app.py

from flask import Flask, request, jsonify
from transformers import pipeline
import torch

app = Flask(__name__)

#  1. MODEL CONFIGURATION 
# Ensure this path matches your unzipped folder name exactly.
MODEL_PATH = "./sentiment_analyzer_pro"

# Load the DistilBERT pipeline. 
# We use device=-1 to ensure it runs on CPU, which is standard for free Hugging Face Spaces.
print("Loading DistilBERT 3-class model...")
try:
    classifier = pipeline(
        "sentiment-analysis", 
        model=MODEL_PATH, 
        tokenizer=MODEL_PATH,
        device=-1 
    )
    print("Model loaded successfully!")
except Exception as e:
    print(f"Error loading model: {e}")

#  2. PREDICTION ENDPOINT 
@app.route('/predict', methods=['POST'])
def predict_endpoint():
    """
    Receives JSON input: {"text": "Your review here"}
    Returns JSON: {"sentiment": "Label", "score": 0.99, "confidence_flag": "High/Low"}
    """
    data = request.get_json()
    
    # Validate input
    if not data or 'text' not in data:
        return jsonify({'error': 'No text provided'}), 400
    
    sentence = data['text']
    
    # Perform inference
    # Result is a list: [{'label': 'POSITIVE', 'score': 0.98}]
    result = classifier(sentence)[0]
    
    label = result['label']
    score = result['score']
    
    #  3. INTELLIGENT SARCASM/MIXED LOGIC 
    # We use 0.70 (70%) as the "Sureness" threshold.
    # If the model is less than 70% confident, we categorize it as Neutral/Mixed.
    # This captures sarcasm where the model sees conflicting emotional signals.
    if score < 0.70:
        final_sentiment = "Neutral / Mixed"
        confidence_flag = "Low"
    else:
        # Standardize labels from 'POSITIVE' to 'Positive'
        final_sentiment = label.capitalize()
        confidence_flag = "High"
    
    return jsonify({
        'sentiment': final_sentiment, 
        'score': round(score, 4),
        'confidence_flag': confidence_flag
    })

#  4. HEALTH CHECK 
@app.route('/', methods=['GET'])
def health_check():
    return "Sentiment Analyzer Pro API is online."

if __name__ == '__main__':
    # Port 7860 is required for Hugging Face Spaces deployment.
    # host='0.0.0.0' allows external connections (like your Chrome Extension).
    app.run(host='0.0.0.0', port=7860)