Create model.py

model.py

@@ -0,0 +1,246 @@
+import numpy as np
+import pandas as pd
+import requests
+from io import StringIO
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import speech_recognition as sr
+import pyttsx3
+from googlesearch import search
+from bs4 import BeautifulSoup
+import urllib.request
+from urllib.parse import quote
+
+class HybridChatBot:
+    def __init__(self, dataset_url=None):
+        self.dataset_url = dataset_url
+        self.qa_pairs = {}
+        self.vectorizer = TfidfVectorizer()
+        self.X = None
+        self.recognizer = sr.Recognizer()
+        self.engine = pyttsx3.init()
+        
+        # Voice engine settings
+        voices = self.engine.getProperty('voices')
+        self.engine.setProperty('voice', voices[0].id)
+        self.engine.setProperty('rate', 150)
+        
+        if dataset_url:
+            self.load_dataset()
+            self.train()
+
+    def load_dataset(self):
+        """Load dataset from web resource"""
+        try:
+            response = requests.get(self.dataset_url)
+            response.raise_for_status()
+            
+            if self.dataset_url.endswith('.csv'):
+                data = pd.read_csv(StringIO(response.text))
+            elif self.dataset_url.endswith('.json'):
+                data = pd.read_json(StringIO(response.text))
+            else:
+                print("File format not supported")
+                return
+                
+            for _, row in data.iterrows():
+                self.qa_pairs[row["question"].lower()] = row["answer"]
+                
+            print(f"Loaded {len(self.qa_pairs)} question-answer pairs")
+            
+        except Exception as e:
+            print(f"Error loading dataset: {e}")
+
+    def train(self):
+        """Train the model on loaded data"""
+        if not self.qa_pairs:
+            print("No data available for training!")
+            return
+        
+        questions = list(self.qa_pairs.keys())
+        self.X = self.vectorizer.fit_transform(questions)
+        print("Model trained on loaded data")
+
+    def add_qa_pair(self, question, answer):
+        """Add new question-answer pair"""
+        self.qa_pairs[question.lower()] = answer
+        self.train()
+
+    def web_search(self, query, num_results=3):
+        """Perform web search and extract information"""
+        try:
+            print(f"\nSearching the web: {query}")
+            search_results = []
+            
+            # Perform Google search
+            for url in search(query, num_results=num_results, lang='en'):
+                try:
+                    # Get page content
+                    req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+                    with urllib.request.urlopen(req, timeout=5) as response:
+                        html = response.read()
+                    
+                    # Parse HTML
+                    soup = BeautifulSoup(html, 'html.parser')
+                    
+                    # Remove unwanted elements (scripts, styles, etc.)
+                    for script in soup(["script", "style", "iframe", "nav", "footer"]):
+                        script.extract()
+                    
+                    # Get page text
+                    text = soup.get_text(separator=' ', strip=True)
+                    text = ' '.join(text.split()[:200])  # Take first 200 words
+                    
+                    search_results.append({
+                        'url': url,
+                        'content': text
+                    })
+                    
+                except Exception as e:
+                    print(f"Error processing {url}: {e}")
+                    continue
+            
+            return search_results
+            
+        except Exception as e:
+            print(f"Search error: {e}")
+            return None
+
+    def get_response(self, user_input):
+        """Get response to user input"""
+        if not self.qa_pairs:
+            return "I'm not trained yet. Please add questions and answers."
+        
+        # Check if user wants to perform web search
+        if "search the web for" in user_input.lower() or "find online" in user_input.lower():
+            query = user_input.replace("search the web for", "").replace("find online", "").strip()
+            search_results = self.web_search(query)
+            if search_results:
+                response = "Here's what I found online:\n"
+                for i, result in enumerate(search_results, 1):
+                    response += f"\n{i}. {result['content']}\n(Source: {result['url']})\n"
+                return response[:2000]  # Limit response length
+            else:
+                return "Couldn't find any information online."
+        
+        # Regular question-answer search
+        user_vec = self.vectorizer.transform([user_input.lower()])
+        similarities = cosine_similarity(user_vec, self.X)
+        best_match_idx = np.argmax(similarities)
+        best_match_score = similarities[0, best_match_idx]
+        
+        if best_match_score > 0.5:
+            best_question = list(self.qa_pairs.keys())[best_match_idx]
+            return self.qa_pairs[best_question]
+        else:
+            return "I don't know the answer to this question. Would you like me to search online? (Say 'search the web for...')"
+
+    def text_to_speech(self, text):
+        """Convert text to speech"""
+        self.engine.say(text)
+        self.engine.runAndWait()
+
+    def speech_to_text(self):
+        """Convert speech from microphone to text"""
+        with sr.Microphone() as source:
+            print("\nSpeak now...")
+            self.recognizer.adjust_for_ambient_noise(source)
+            try:
+                audio = self.recognizer.listen(source, timeout=5)
+                text = self.recognizer.recognize_google(audio, language="en-US")
+                print(f"Recognized: {text}")
+                return text
+            except sr.UnknownValueError:
+                print("Speech not recognized")
+                return None
+            except sr.RequestError:
+                print("Recognition service error")
+                return None
+            except sr.WaitTimeoutError:
+                print("Timeout expired")
+                return None
+
+    def run(self):
+        """Improved interaction interface"""
+        print("\n" + "="*50)
+        print("WELCOME TO INTELLIGENT CHATBOT".center(50))
+        print("="*50)
+        
+        current_mode = "text"
+        while True:
+            print("\n" + "-"*50)
+            print(f"Current input mode: {current_mode.upper()}")
+            print("[1] Send text message")
+            print("[2] Speak to the bot")
+            print("[3] Switch input mode")
+            print("[4] Teach the bot a new answer")
+            print("[5] Web search")
+            print("[6] Exit")
+            
+            try:
+                choice = input("Choose action (1-6): ").strip()
+                
+                if choice == "1":
+                    user_input = input("\nYour message: ")
+                    if user_input.lower() in ["exit", "stop"]:
+                        break
+                        
+                    response = self.get_response(user_input)
+                    if response:
+                        print(f"\nBot: {response}")
+                        self.text_to_speech(response)
+                    else:
+                        print("\nBot: I don't know what to say. Would you like to teach me?")
+                
+                elif choice == "2":
+                    user_input = self.speech_to_text()
+                    if user_input:
+                        if user_input.lower() in ["exit", "stop"]:
+                            break
+                            
+                        response = self.get_response(user_input)
+                        if response:
+                            print(f"\nBot: {response}")
+                            self.text_to_speech(response)
+                        else:
+                            print("\nBot: I don't know how to respond to that.")
+                            self.text_to_speech("I don't know how to respond to that")
+                
+                elif choice == "3":
+                    current_mode = "voice" if current_mode == "text" else "text"
+                    print(f"\nMode changed to: {current_mode.upper()}")
+                
+                elif choice == "4":
+                    print("\nTeaching the bot:")
+                    question = input("Enter question: ")
+                    answer = input("Enter answer: ")
+                    self.add_qa_pair(question, answer)
+                    print("Bot successfully trained!")
+                
+                elif choice == "5":
+                    query = input("\nEnter search query: ")
+                    search_results = self.web_search(query)
+                    if search_results:
+                        print("\nSearch results:")
+                        for i, result in enumerate(search_results, 1):
+                            print(f"\n{i}. {result['content']}\n(Source: {result['url']})\n")
+                    else:
+                        print("\nNothing found.")
+                
+                elif choice == "6":
+                    print("\nShutting down...")
+                    break
+                
+                else:
+                    print("\nPlease choose an option between 1 and 6")
+            
+            except KeyboardInterrupt:
+                print("\nShutting down...")
+                break
+
+if __name__ == "__main__":
+   
+    DATASET_URL = "https://raw.githubusercontent.com/user/repo/main/qa_data.csv"
+    
+    bot = HybridChatBot(DATASET_URL)
+    bot.run()