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| import os | |
| import gradio as gr | |
| import keras_hub | |
| import os | |
| import re | |
| model_path="kaggle://asunsada/gemma2_2b_it_en_roleplay/keras/football_coach_11042024_epoch15" # kaggle | |
| class GemmaChatbot: | |
| def __init__(self): | |
| # Initialize the model | |
| #preset = "gemma_instruct_2b_en" # name of pretrained Gemma 2 | |
| #self.gemma_llm = keras_hub.models.GemmaCausalLM.from_preset(preset) | |
| # Load your custom model | |
| self.gemma_llm = keras_hub.models.GemmaCausalLM.from_preset(model_path) | |
| print(self.gemma_llm) | |
| def format_prompt(self, message, history): | |
| # Format conversation history into a single string | |
| formatted_history = "" | |
| for user_msg, assistant_msg in history: | |
| formatted_history += f"User: {user_msg}\nAssistant: {assistant_msg}\n" | |
| # Add the current message | |
| prompt = formatted_history + f"User: {message}\nAssistant:" | |
| return prompt | |
| def generate_response(self, message, history): | |
| # Format the prompt with history | |
| #prompt = self.format_prompt(message, history) | |
| prompt= message | |
| # Generate response | |
| output = self.gemma_llm.generate(prompt,256) | |
| output = clean_incomplete_sentences(output) # remove incomplete sentences (typically | |
| # last one or any question at the end. | |
| return output.replace(prompt, "").strip() | |
| # remove incomplete sentences (typically last one or any question at the end.) | |
| def clean_incomplete_sentences(text): | |
| # Split text into sentences using regular expressions | |
| sentences = re.split(r'(?<=\.) |(?<=\?) |(?<=!) ', text) | |
| # Filter out incomplete sentences (those not ending with ".", "?" or "!") | |
| complete_sentences = [s for s in sentences if re.search(r'[.!?]$', s)] | |
| # Remove the last sentence if it ends with a question mark | |
| if complete_sentences and complete_sentences[-1].endswith('?'): | |
| complete_sentences = complete_sentences[:-1] | |
| # Join sentences back into a single string | |
| cleaned_text = ' '.join(complete_sentences) | |
| return cleaned_text | |
| def create_chatbot(): | |
| # Initialize the chatbot | |
| chatbot = GemmaChatbot() | |
| # Create the Gradio interface | |
| chat_interface = gr.ChatInterface( | |
| fn=chatbot.generate_response, | |
| title="🏈 Tackle Tutor Chatbot 🏈 💬", | |
| description="I'm Tackle Tutor, the head coach of the greatest football team around! " | |
| "With over 20 years of coaching experience and numerous championships under my belt, " | |
| "I've also had the honor of coaching and playing in the NFL. \n\n" | |
| "Ask me how to tackle any challenge, on the field or in life, and I'll guide you through it", | |
| examples=[ | |
| "Why is resilience important in football and life?", | |
| "How can I keep trying when something is really hard?", | |
| "How do famous coaches inspire?", | |
| "What can I learn from coach Barry Switzer?", | |
| "How can football teach us about teamwork?", | |
| "Why is preparation essential for success?", | |
| "Why should we always give our best effort?", | |
| ], | |
| theme=gr.themes.Soft() | |
| ) | |
| return chat_interface | |
| # Launch the chatbot | |
| if __name__ == "__main__": | |
| # Create and launch the interface | |
| chat_interface = create_chatbot() | |
| chat_interface.launch(share=True) |