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from flask import Flask, request, render_template_string, jsonify, send_from_directory |
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import requests |
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import pandas as pd |
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import re |
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import time |
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from random import randint, choice |
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import os |
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from transformers import XLMRobertaForSequenceClassification, XLMRobertaTokenizer |
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from peft import PeftModel, PeftConfig |
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import torch |
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from collections import defaultdict |
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flask_app = Flask(__name__) |
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tokenizer = XLMRobertaTokenizer.from_pretrained("letijo03/lora-adapter-32",use_fast=True, trust_remote_code=True) |
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base_model = XLMRobertaForSequenceClassification.from_pretrained("xlm-roberta-base", num_labels=3) |
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config = PeftConfig.from_pretrained("letijo03/lora-adapter-32") |
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model = PeftModel.from_pretrained(base_model, "letijo03/lora-adapter-32") |
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model.eval() |
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def classify_sentiment(text): |
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512) |
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outputs = model(**inputs) |
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prediction = torch.argmax(outputs.logits, dim=-1) |
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return prediction.item() |
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html_template = """ |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>Comment Sentiment Analysis</title> |
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<style> |
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body { font-family: Arial, sans-serif; background-color: #f5f5f5; margin: 0; padding: 0; color: #333; } |
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header { background-color: #FF5722; color: white; padding: 20px; text-align: center; } |
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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); } |
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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); } |
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textarea, button { padding: 12px; font-size: 1.1em; border: 1px solid #ccc; border-radius: 6px; } |
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textarea { background-color: #fff; resize: vertical; min-height: 100px; } |
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button { background-color: #FF5722; color: white; border: none; cursor: pointer; transition: background-color 0.3s ease; } |
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button:hover { background-color: #E64A19; } |
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.result-message { text-align: center; margin-top: 20px; font-size: 18px; font-weight: bold; } |
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</style> |
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<script> |
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document.addEventListener("DOMContentLoaded", function() { |
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document.getElementById("commentForm").onsubmit = async function(e) { |
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e.preventDefault(); |
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const comment = document.getElementById("comment").value; |
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const resultDiv = document.getElementById("result"); |
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resultDiv.innerHTML = ""; |
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try { |
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const response = await fetch('/analyze', { |
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method: 'POST', |
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headers: { 'Content-Type': 'application/x-www-form-urlencoded' }, |
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body: new URLSearchParams({ 'comment': comment }) |
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}); |
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const data = await response.json(); |
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if (data.error) { |
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resultDiv.innerHTML = `<p class="result-message" style="color:red;">${data.error}</p>`; |
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} else { |
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resultDiv.innerHTML = `<p class="result-message" style="color:green;">${data.message}</p>`; |
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} |
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} catch (error) { |
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resultDiv.innerHTML = `<p class="result-message" style="color:red;">Error sending request: ${error.message}</p>`; |
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console.error('Fetch error:', error); |
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} |
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}; |
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}); |
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</script> |
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</head> |
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<body> |
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<header> |
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<h1>Comment Sentiment Analysis</h1> |
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</header> |
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<main> |
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<form id="commentForm"> |
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<label for="comment">Enter your comment:</label> |
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<textarea id="comment" name="comment" placeholder="Type your comment here..." required></textarea> |
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<button type="submit">Analyze Sentiment</button> |
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</form> |
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<div id="result"></div> |
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</main> |
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</body> |
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</html> |
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""" |
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@flask_app.route('/') |
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def index(): |
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return render_template_string(html_template) |
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@flask_app.route('/analyze', methods=['POST']) |
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def analyze(): |
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comment = request.form.get('comment') |
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if not comment or comment.strip() == "": |
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return jsonify({'error': 'Please provide a valid comment.'}) |
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sentiment = classify_sentiment(comment) |
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sentiment_label = "Positive" if sentiment == 2 else "Neutral" if sentiment == 1 else "Negative" |
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return jsonify({'message': f'Sentiment analysis complete. The sentiment is: {sentiment_label}.'}) |
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from asgiref.wsgi import WsgiToAsgi |
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app = WsgiToAsgi(flask_app) |
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if __name__ == '__main__': |
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import uvicorn |
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uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 7860))) |
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