Delete grammar_chatbot.py

grammar_chatbot.py

@@ -1,159 +0,0 @@
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-import random
-from typing import Dict, List
-
-class GrammarChatbot:
-    def __init__(self):
-        """Initialize models for fluent corrections"""
-        # Initialize models
-        self.grammar_model = pipeline(
-            "text2text-generation",
-            model="vennify/t5-base-grammar-correction",
-            device=0 if torch.cuda.is_available() else -1
-        )
-
-        # Fluent paraphrasing model
-        self.paraphrase_tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
-        self.paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
-
-        # Enhanced conversation components
-        self.compliments = [
-            "Great job! Your English is improving!",
-            "Nice improvement! Keep it up!",
-            "You're doing well with your practice!",
-            "Good effort! I can see you're trying hard!",
-            "Excellent try! You're getting better!",
-            "Well done! That was much better!",
-            "Impressive! Your sentence structure is improving!"
-        ]
-
-        # Organized question bank by categories
-        self.question_categories = {
-            "daily_life": [
-                "What did you do this morning?",
-                "How do you usually spend your evenings?",
-                "What's your morning routine like?",
-                "Do you have any plans for this weekend?",
-                "What time do you usually wake up?"
-            ],
-            "hobbies": [
-                "What hobbies do you enjoy in your free time?",
-                "Have you picked up any new hobbies recently?",
-                "Do you prefer indoor or outdoor activities?",
-                "What's something you've always wanted to try?",
-                "Do you play any musical instruments?"
-            ],
-            "food": [
-                "What's your favorite comfort food?",
-                "Do you enjoy cooking? What's your specialty?",
-                "What's the most unusual food you've ever tried?",
-                "Do you prefer sweet or savory snacks?",
-                "What's your go-to breakfast?"
-            ],
-            "travel": [
-                "If you could visit any country, where would you go?",
-                "What's the most beautiful place you've ever seen?",
-                "Do you prefer beach vacations or city trips?",
-                "What's your favorite travel memory?",
-                "What's the next place you'd like to visit?"
-            ],
-            "technology": [
-                "How do you use technology in your daily life?",
-                "What's your opinion about social media?",
-                "Do you think AI will change our lives significantly?",
-                "What tech gadget couldn't you live without?",
-                "How has technology changed your work/studies?"
-            ],
-            "future": [
-                "Where do you see yourself in five years?",
-                "What's one skill you'd like to learn?",
-                "Do you have any big goals for this year?",
-                "What would your perfect day look like?",
-                "What's something you want to achieve?"
-            ]
-        }
-
-        self.current_question = None
-        self.current_category = None
-        self.conversation_history = []
-
-    def correct_grammar(self, text: str) -> str:
-        """Basic grammar correction"""
-        result = self.grammar_model(
-            text,
-            max_length=256,
-            num_beams=4,
-            early_stopping=True
-        )
-        return result[0]['generated_text']
-
-    def fluent_correction(self, text: str) -> str:
-        """Create fluent, natural rewrites"""
-        input_ids = self.paraphrase_tokenizer(
-            f"paraphrase: {text}",
-            return_tensors="pt",
-            max_length=256,
-            truncation=True
-        ).input_ids
-
-        outputs = self.paraphrase_model.generate(
-            input_ids,
-            temperature=0.7,
-            max_length=256,
-            num_beams=5,
-            early_stopping=True
-        )
-
-        return self.paraphrase_tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    def _get_next_question(self) -> str:
-        """Select next question based on conversation context"""
-        # If we're continuing a category, use follow-up questions
-        if self.current_category and random.random() < 0.6:  # 60% chance to stay on topic
-            return random.choice(self.question_categories[self.current_category])
-
-        # Otherwise select a new random category
-        self.current_category = random.choice(list(self.question_categories.keys()))
-        return random.choice(self.question_categories[self.current_category])
-
-    def generate_response(self, user_input: str) -> Dict:
-        """Generate a conversational response"""
-        # Store conversation history
-        self.conversation_history.append(("user", user_input))
-
-        # Correct the input
-        corrected = self.fluent_correction(user_input)
-        is_corrected = corrected.lower() != user_input.lower()
-
-        # Generate response
-        response = {
-            "original_text": user_input,
-            "corrected_text": corrected,
-            "is_corrected": is_corrected,
-            "compliment": random.choice(self.compliments) if is_corrected else "",
-            "next_question": self._get_next_question(),
-            "end_conversation": False
-        }
-
-        # Update state
-        self.current_question = response["next_question"]
-        self.conversation_history.append(("bot", response["next_question"]))
-
-        return response
-
-    def start_conversation(self) -> Dict:
-        """Initialize a new conversation"""
-        self.conversation_history = []
-        self.current_category = random.choice(list(self.question_categories.keys()))
-        self.current_question = random.choice(self.question_categories[self.current_category])
-
-        return {
-            "message": "Hello! I'm your English practice partner. Let's chat!",
-            "next_question": self.current_question,
-            "end_conversation": False
-        }
-
-    def get_conversation_history(self) -> List[tuple]:
-        """Get the complete conversation history"""
-        return self.conversation_history