| | 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 |
| | import torch |
| | from collections import defaultdict |
| |
|
| |
|
| | |
| | flask_app = Flask(__name__) |
| |
|
| | |
| | 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() |
| |
|
| | 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 = """ |
| | <!DOCTYPE html> |
| | <html lang="en"> |
| | <head> |
| | <meta charset="UTF-8"> |
| | <meta name="viewport" content="width=device-width, initial-scale=1.0"> |
| | <title>Comment Sentiment Analysis</title> |
| | <style> |
| | body { font-family: Arial, sans-serif; background-color: #f5f5f5; margin: 0; padding: 0; color: #333; } |
| | header { background-color: #FF5722; color: white; padding: 20px; text-align: center; } |
| | main { padding: 20px; max-width: 900px; margin: 0 auto; background-color: white; border-radius: 8px; box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1); } |
| | form { margin: 20px auto; max-width: 600px; display: flex; flex-direction: column; gap: 15px; background-color: #f9f9f9; padding: 20px; border-radius: 8px; box-shadow: 0px 2px 4px rgba(0, 0, 0, 0.1); } |
| | textarea, button { padding: 12px; font-size: 1.1em; border: 1px solid #ccc; border-radius: 6px; } |
| | textarea { background-color: #fff; resize: vertical; min-height: 100px; } |
| | button { background-color: #FF5722; color: white; border: none; cursor: pointer; transition: background-color 0.3s ease; } |
| | button:hover { background-color: #E64A19; } |
| | .result-message { text-align: center; margin-top: 20px; font-size: 18px; font-weight: bold; } |
| | </style> |
| | <script> |
| | document.addEventListener("DOMContentLoaded", function() { |
| | document.getElementById("commentForm").onsubmit = async function(e) { |
| | e.preventDefault(); |
| | const comment = document.getElementById("comment").value; |
| | const resultDiv = document.getElementById("result"); |
| | resultDiv.innerHTML = ""; |
| | try { |
| | const response = await fetch('/analyze', { |
| | method: 'POST', |
| | headers: { 'Content-Type': 'application/x-www-form-urlencoded' }, |
| | body: new URLSearchParams({ 'comment': comment }) |
| | }); |
| | const data = await response.json(); |
| | if (data.error) { |
| | resultDiv.innerHTML = `<p class="result-message" style="color:red;">${data.error}</p>`; |
| | } else { |
| | resultDiv.innerHTML = `<p class="result-message" style="color:green;">${data.message}</p>`; |
| | } |
| | } catch (error) { |
| | resultDiv.innerHTML = `<p class="result-message" style="color:red;">Error sending request: ${error.message}</p>`; |
| | console.error('Fetch error:', error); |
| | } |
| | }; |
| | }); |
| | </script> |
| | </head> |
| | <body> |
| | <header> |
| | <h1>Comment Sentiment Analysis</h1> |
| | </header> |
| | <main> |
| | <form id="commentForm"> |
| | <label for="comment">Enter your comment:</label> |
| | <textarea id="comment" name="comment" placeholder="Type your comment here..." required></textarea> |
| | <button type="submit">Analyze Sentiment</button> |
| | </form> |
| | <div id="result"></div> |
| | </main> |
| | </body> |
| | </html> |
| | """ |
| |
|
| | @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}.'}) |
| |
|
| | |
| | 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))) |
| |
|
