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faq_system.py

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+# CodeBasics FAQ System
+# Smart FAQ retrieval using TF-IDF and cosine similarity
+
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+class CodeBasicsFAQ:
+    def __init__(self, csv_path='codebasics_faqs.csv'):
+        """Initialize FAQ system from CSV file"""
+        # Load FAQ data
+        encodings = ['utf-8', 'latin-1', 'iso-8859-1', 'cp1252']
+        df = None
+        
+        for encoding in encodings:
+            try:
+                df = pd.read_csv(csv_path, encoding=encoding)
+                print(f"✅ Loaded {len(df)} FAQs")
+                break
+            except:
+                continue
+        
+        if df is None:
+            raise Exception("Could not load FAQ CSV")
+        
+        self.questions = df['prompt'].tolist()
+        self.answers = df['response'].tolist()
+        
+        # Create TF-IDF vectorizer
+        self.vectorizer = TfidfVectorizer(
+            lowercase=True,
+            stop_words='english',
+            ngram_range=(1, 2)
+        )
+        
+        # Fit on all questions
+        self.question_vectors = self.vectorizer.fit_transform(self.questions)
+        print(f"✅ FAQ System ready!")
+    
+    def find_best_match(self, query, threshold=0.2):
+        """Find best matching FAQ"""
+        query_vector = self.vectorizer.transform([query])
+        similarities = cosine_similarity(query_vector, self.question_vectors)[0]
+        
+        best_idx = np.argmax(similarities)
+        best_score = similarities[best_idx]
+        
+        if best_score >= threshold:
+            return {
+                'question': self.questions[best_idx],
+                'answer': self.answers[best_idx],
+                'confidence': best_score
+            }
+        return None
+    
+    def answer(self, query):
+        """Get answer for a query"""
+        result = self.find_best_match(query)
+        
+        if result:
+            return {
+                'status': 'success',
+                'confidence': f"{result['confidence']*100:.1f}%",
+                'matched_question': result['question'],
+                'answer': result['answer']
+            }
+        else:
+            return {
+                'status': 'no_match',
+                'message': 'No matching FAQ found. Try rephrasing your question.'
+            }
+    
+    def search_keyword(self, keyword):
+        """Search FAQs by keyword"""
+        keyword_lower = keyword.lower()
+        matches = []
+        
+        for i, q in enumerate(self.questions):
+            if keyword_lower in q.lower() or keyword_lower in self.answers[i].lower():
+                matches.append({
+                    'question': q,
+                    'answer': self.answers[i]
+                })
+        
+        return matches
+    
+    def list_all_questions(self):
+        """Return all FAQ questions"""
+        return self.questions
+
+
+# ============================================================================
+# USAGE EXAMPLE
+# ============================================================================
+
+if __name__ == "__main__":
+    # Initialize
+    faq = CodeBasicsFAQ('codebasics_faqs.csv')
+    
+    # Example questions
+    test_questions = [
+        "Can I take this bootcamp without programming experience?",
+        "Why should I trust Codebasics?",
+        "What are the prerequisites?",
+        "Do I need a laptop?"
+    ]
+    
+    print("\n" + "="*70)
+    print("TESTING FAQ SYSTEM")
+    print("="*70 + "\n")
+    
+    for question in test_questions:
+        print(f"❓ {question}")
+        result = faq.answer(question)
+        
+        if result['status'] == 'success':
+            print(f"✅ Match: {result['confidence']}")
+            print(f"📝 Q: {result['matched_question']}")
+            print(f"💡 A: {result['answer'][:100]}...\n")
+        else:
+            print(f"❌ {result['message']}\n")
+    
+    # Interactive mode
+    print("\n" + "="*70)
+    print("INTERACTIVE MODE")
+    print("="*70)
+    print("Type 'quit' to exit\n")
+    
+    while True:
+        user_q = input("❓ Your question: ").strip()
+        
+        if user_q.lower() in ['quit', 'exit', 'q']:
+            print("👋 Goodbye!")
+            break
+        
+        if not user_q:
+            continue
+        
+        result = faq.answer(user_q)
+        
+        if result['status'] == 'success':
+            print(f"\n[Confidence: {result['confidence']}]")
+            print(f"\n📌 {result['matched_question']}")
+            print(f"\n✨ {result['answer']}\n")
+        else:
+            print(f"\n❌ {result['message']}\n")