Update main.py

main.py

@@ -1,101 +1,61 @@
+import os
 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
+matplotlib.use('Agg')  # Prevents GUI issues for Matplotlib
 import matplotlib.pyplot as plt
-
 import base64
 from io import BytesIO
 
+# Fix Permission Issues: Set Writable Directories for Hugging Face & Matplotlib
+os.environ["HF_HOME"] = "/tmp"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp"
+os.environ["MPLCONFIGDIR"] = "/tmp"
 
-app = Flask(__name__)
-
+# Create directories if they don’t exist
+os.makedirs(os.environ["HF_HOME"], exist_ok=True)
+os.makedirs(os.environ["TRANSFORMERS_CACHE"], exist_ok=True)
+os.makedirs(os.environ["MPLCONFIGDIR"], exist_ok=True)
 
-model_name = "philipobiorah/bert-imdb-model"
+app = Flask(__name__)
 
-# Load tokenizer and model
+# Load Model from Hugging Face (Ensure the model is accessible)
+MODEL_NAME = "philipobiorah/bert-imdb-model"
 tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertForSequenceClassification.from_pretrained(model_name)
-
+model = BertForSequenceClassification.from_pretrained(MODEL_NAME)
 
 model.eval()
 
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-
+# Function to Predict Sentiment
 def predict_sentiment(text):
-    # Split the text into chunks of 512 tokens
-    tokenized_text = tokenizer.encode(text, add_special_tokens=True)
-    chunks = [tokenized_text[i:i + 512] for i in range(0, len(tokenized_text), 512)]
+    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)
-        inputs = tokenizer.decode(chunk, skip_special_tokens=True, clean_up_tokenization_spaces=True)  # Explicitly set clean_up_tokenization_spaces
+        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 the predictions (majority voting)
-    sentiment_counts = Counter(sentiments)
-    majority_sentiment = sentiment_counts.most_common(1)[0][0]
+    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():
-    if request.method == 'POST':
-        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 plot
-        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')
-        
-        # Save the plot to a BytesIO object
-        img = BytesIO()
-        plt.savefig(img, format='png', bbox_inches='tight')
-        img.seek(0)
-
-        # Encode the image in base64 and decode it to UTF-8
-        plot_url = base64.b64encode(img.getvalue()).decode('utf8')
-
-        # Close the plot to free memory
-        plt.close(fig)
-
-        return render_template('result.html', tables=[data.to_html(classes='data')], titles=data.columns.values, summary=summary, plot_url=plot_url)
-
-
+    text = request.form['text']
+    sentiment = predict_sentiment(text)
+    return render_template('upload.html', sentiment=sentiment)
 
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=7860, debug=True)
-
-
-