from flask import Flask, request, render_template_string, jsonify, send_from_directory
import requests
import pandas as pd
import re
import time
from random import randint, choice
import os
from transformers import XLMRobertaForSequenceClassification, XLMRobertaTokenizer
from peft import PeftModel, PeftConfig # Ensure peft library is installed
import torch
from collections import defaultdict
# Define the Flask app
flask_app = Flask(__name__)
#Load the base XLM-RoBERTa model with the correct number of labels (3 labels for classification)
tokenizer = XLMRobertaTokenizer.from_pretrained("letijo03/lora-adapter-32",use_fast=True, trust_remote_code=True)
base_model = XLMRobertaForSequenceClassification.from_pretrained("xlm-roberta-base", num_labels=3)
config = PeftConfig.from_pretrained("letijo03/lora-adapter-32")
model = PeftModel.from_pretrained(base_model, "letijo03/lora-adapter-32")
model.eval() # Set the model to evaluation mode
def classify_sentiment(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=-1)
return prediction.item()
# HTML template for user input
html_template = """
Comment Sentiment Analysis
Comment Sentiment Analysis
"""
@flask_app.route('/')
def index():
return render_template_string(html_template)
@flask_app.route('/analyze', methods=['POST'])
def analyze():
comment = request.form.get('comment')
if not comment or comment.strip() == "":
return jsonify({'error': 'Please provide a valid comment.'})
sentiment = classify_sentiment(comment)
sentiment_label = "Positive" if sentiment == 2 else "Neutral" if sentiment == 1 else "Negative"
return jsonify({'message': f'Sentiment analysis complete. The sentiment is: {sentiment_label}.'})
# Wrap the Flask app as an ASGI app so that the module-level variable 'app' is ASGI-compatible
from asgiref.wsgi import WsgiToAsgi
app = WsgiToAsgi(flask_app)
if __name__ == '__main__':
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 7860)))