Create app.py

app.py

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+from flask import Flask, request, render_template
+import pandas as pd
+import spacy
+from transformers import pipeline
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load spaCy model for preprocessing
+nlp = spacy.load("en_core_web_sm")
+
+# Load Hugging Face pipelines
+sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+ner_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english", aggregation_strategy="simple")
+
+# Function to preprocess text
+def preprocess_text(text):
+    doc = nlp(text)
+    tokens = [token.lemma_.lower() for token in doc if not token.is_stop and not token.is_punct]
+    return ' '.join(tokens)
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    if request.method == 'POST':
+        comments = request.form['comments']
+        cleaned_comments = preprocess_text(comments)
+
+        # Analyze sentiment
+        sentiment_result = sentiment_pipeline(cleaned_comments)[0]
+
+        # Analyze entities
+        entities_result = ner_pipeline(cleaned_comments)
+
+        # Prepare results for rendering
+        result = {
+            'original_comment': comments,
+            'cleaned_comment': cleaned_comments,
+            'sentiment': sentiment_result,
+            'entities': entities_result
+        }
+
+        return render_template('result.html', result=result)
+
+if __name__ == '__main__':
+    app.run(debug=True)