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| import os | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from PIL import Image | |
| class XylariaChat: | |
| def __init__(self): | |
| # Securely load HuggingFace token | |
| self.hf_token = os.getenv("HF_TOKEN") | |
| if not self.hf_token: | |
| raise ValueError("HuggingFace token not found in environment variables") | |
| # Initialize the inference clients | |
| self.chat_client = InferenceClient( | |
| model="Qwen/QwQ-32B-Preview", | |
| api_key=self.hf_token | |
| ) | |
| self.image_client = InferenceClient( | |
| model="SG161222/RealVisXL_V4.0", | |
| api_key=self.hf_token | |
| ) | |
| # Initialize conversation history and persistent memory | |
| self.conversation_history = [] | |
| self.persistent_memory = {} | |
| def get_response(self, user_input): | |
| """Get text-based response from the model.""" | |
| messages = [ | |
| {"role": "system", "content": "Your name is Xylaria 1.4 Senoa, an advanced ai model developed by sk md saad amin"}, | |
| *self.conversation_history, | |
| {"role": "user", "content": user_input} | |
| ] | |
| try: | |
| response_stream = self.chat_client.chat.completions.create( | |
| messages=messages, | |
| temperature=0.5, | |
| max_tokens=10240, | |
| top_p=0.7, | |
| stream=True | |
| ) | |
| return response_stream | |
| except Exception as e: | |
| return f"Error generating response: {str(e)}" | |
| def generate_image(self, prompt): | |
| """Generate image based on prompt.""" | |
| try: | |
| # Create an image from the prompt | |
| image = self.image_client.text_to_image(prompt) | |
| return image | |
| except Exception as e: | |
| return f"Error generating image: {str(e)}" | |
| def create_interface(self): | |
| def streaming_response(message, chat_history): | |
| """Handle text response streaming.""" | |
| response_stream = self.get_response(message) | |
| if isinstance(response_stream, str): | |
| return "", chat_history + [[message, response_stream]] | |
| full_response = "" | |
| updated_history = chat_history + [[message, ""]] | |
| for chunk in response_stream: | |
| if chunk.choices[0].delta.content: | |
| full_response += chunk.choices[0].delta.content | |
| updated_history[-1][1] = full_response | |
| yield "", updated_history | |
| self.conversation_history.append({"role": "user", "content": message}) | |
| self.conversation_history.append({"role": "assistant", "content": full_response}) | |
| if len(self.conversation_history) > 10: | |
| self.conversation_history = self.conversation_history[-10:] | |
| def generate_image_response(prompt): | |
| """Handle image generation.""" | |
| if not prompt.strip(): | |
| return None | |
| return self.generate_image(prompt) | |
| with gr.Blocks() as demo: | |
| chatbot = gr.Chatbot(label="Xylaria 1.4 Senoa", height=500) | |
| with gr.Row(): | |
| txt = gr.Textbox(show_label=False, placeholder="Type your message...", scale=8) | |
| send_btn = gr.Button("💬", scale=1) | |
| img_btn = gr.Button("🖼️", scale=1) | |
| clear_btn = gr.Button("Clear Conversation") | |
| clear_memory_btn = gr.Button("Clear Memory") | |
| send_btn.click(fn=streaming_response, inputs=[txt, chatbot], outputs=[txt, chatbot]) | |
| txt.submit(fn=streaming_response, inputs=[txt, chatbot], outputs=[txt, chatbot]) | |
| img_btn.click(fn=generate_image_response, inputs=txt, outputs=chatbot) | |
| clear_btn.click(fn=lambda: None, inputs=None, outputs=chatbot) | |
| clear_memory_btn.click(fn=lambda: None, inputs=None, outputs=[]) | |
| return demo | |
| # Launch the interface | |
| def main(): | |
| chat = XylariaChat() | |
| interface = chat.create_interface() | |
| interface.launch(share=True, debug=True) | |
| if __name__ == "__main__": | |
| main() | |