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from flask import Flask, request, send_file, jsonify
from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
import pandas as pd
import torch
import tempfile
import os
import re
from collections import Counter
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
# Load model from Hugging Face Hub
model_name = "AbdoIR/x-sentiment-analysis/fine_tuned_model"
model = DistilBertForSequenceClassification.from_pretrained(model_name)
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
# Predict sentiment
def predict_sentiment(texts):
encodings = tokenizer(texts, truncation=True, padding=True, max_length=128, return_tensors="pt")
encodings = {key: val.to(device) for key, val in encodings.items()}
with torch.no_grad():
outputs = model(**encodings)
predictions = torch.argmax(outputs.logits, dim=1)
sentiment_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
return [sentiment_map[p.item()] for p in predictions]
# Top frequent words
def get_top_words(texts, n=30):
all_words = []
for text in texts:
tokens = re.findall(r'\b\w{3,}\b', str(text).lower())
all_words.extend(tokens)
counter = Counter(all_words)
most_common = counter.most_common(n)
return pd.DataFrame(most_common, columns=['word', 'count'])
# POST /predict
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return jsonify({'error': 'No file uploaded'}), 400
file = request.files['file']
try:
df = pd.read_csv(file)
except Exception:
try:
file.seek(0)
df = pd.read_excel(file)
except Exception:
return jsonify({'error': 'Unable to read the file'}), 400
if 'content' in df.columns:
text_col = 'content'
elif 'tweet' in df.columns:
text_col = 'tweet'
else:
return jsonify({'error': 'No "content" or "tweet" column found'}), 400
texts = df[text_col].astype(str).tolist()
df['sentiment'] = predict_sentiment(texts)
top_words_df = get_top_words(texts)
temp_dir = tempfile.mkdtemp()
sentiment_path = os.path.join(temp_dir, 'final_data.csv')
df.to_csv(sentiment_path, index=False)
words_path = os.path.join(temp_dir, 'word_frequent.csv')
top_words_df.to_csv(words_path, index=False)
return jsonify({
'sentiment_file': f'/download?file={sentiment_path}',
'top_words_file': f'/download?file={words_path}',
'sentiment_data': df.to_dict(orient='records'),
'top_words_data': top_words_df.to_dict(orient='records')
})
# GET /download
@app.route('/download')
def download():
file_path = request.args.get('file')
if not file_path or not os.path.exists(file_path):
return jsonify({'error': 'File not found'}), 404
return send_file(file_path, as_attachment=True)
if __name__ == '__main__':
app.run(host="0.0.0.0", port=5000, debug=True)
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