File size: 4,821 Bytes
fcb8cc9 6cfdd4c fcb8cc9 45cf1af 6cfdd4c 249726c fcb8cc9 6cfdd4c cf5b03b 6cfdd4c cf5b03b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | 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 = """
<!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}.'})
# 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)))
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