Delete main.py

main.py

@@ -1,91 +0,0 @@
-from flask import Flask, request, render_template
-import pandas as pd
-import torch
-from transformers import BertTokenizer, BertForSequenceClassification
-from collections import Counter
-import matplotlib
-matplotlib.use('Agg')  # Set the backend before importing pyplot
-import matplotlib.pyplot as plt
-import base64
-from io import BytesIO
-import os
-
-app = Flask(__name__)
-
-# Load Model - Check if local model exists; otherwise, load from Hugging Face
-MODEL_PATH = "bert_imdb_model.bin"
-MODEL_HF_REPO = "philipobiorah/bert-imdb-model"  # Replace with your Hugging Face model repo
-
-if os.path.exists(MODEL_PATH):
-    print("Loading model from local file...")
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
-    model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device('cpu')))
-else:
-    print("Loading model from Hugging Face Hub...")
-    model = BertForSequenceClassification.from_pretrained(MODEL_HF_REPO)
-
-model.eval()
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-
-def predict_sentiment(text):
-    # Tokenize and split into chunks
-    tokens = tokenizer.encode(text, add_special_tokens=True)
-    chunks = [tokens[i:i + 512] for i in range(0, len(tokens), 512)]
-
-    # Predict sentiment for each chunk
-    sentiments = []
-    for chunk in chunks:
-        inputs = tokenizer.decode(chunk, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        inputs = tokenizer(inputs, return_tensors="pt", truncation=True, padding=True, max_length=512)
-        
-        with torch.no_grad():
-            outputs = model(**inputs)
-        
-        sentiments.append(outputs.logits.argmax(dim=1).item())
-
-    # Aggregate sentiment results (majority voting)
-    majority_sentiment = Counter(sentiments).most_common(1)[0][0]
-    return 'Positive' if majority_sentiment == 1 else 'Negative'
-
-@app.route('/')
-def upload_file():
-    return render_template('upload.html')
-
-@app.route('/analyze_text', methods=['POST'])
-def analyze_text():
-    text = request.form['text']
-    sentiment = predict_sentiment(text)
-    return render_template('upload.html', sentiment=sentiment)
-
-@app.route('/uploader', methods=['GET', 'POST'])
-def upload_file_post():
-    if request.method == 'POST':
-        f = request.files['file']
-        data = pd.read_csv(f)
-
-        # Predict sentiment for each review
-        data['sentiment'] = data['review'].apply(predict_sentiment)
-
-        # Sentiment Analysis Summary
-        sentiment_counts = data['sentiment'].value_counts().to_dict()
-        summary = f"Total Reviews: {len(data)}<br>" \
-                  f"Positive: {sentiment_counts.get('Positive', 0)}<br>" \
-                  f"Negative: {sentiment_counts.get('Negative', 0)}<br>"
-
-        # Generate bar chart
-        fig, ax = plt.subplots()
-        ax.bar(sentiment_counts.keys(), sentiment_counts.values(), color=['red', 'blue'])
-        ax.set_ylabel('Counts')
-        ax.set_title('Sentiment Analysis Summary')
-
-        # Convert plot to base64 for embedding
-        img = BytesIO()
-        plt.savefig(img, format='png', bbox_inches='tight')
-        img.seek(0)
-        plot_url = base64.b64encode(img.getvalue()).decode('utf8')
-        plt.close(fig)
-
-        return render_template('result.html', tables=[data.to_html(classes='data')], titles=data.columns.values, summary=summary, plot_url=plot_url)
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860, debug=True)