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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| # Prepare the messages, starting with the system message | |
| messages = [{"role": "system", "content": system_message}] | |
| # Add the conversation history to the messages | |
| for user_message, assistant_response in history: | |
| if user_message: | |
| messages.append({"role": "user", "content": user_message}) | |
| if assistant_response: | |
| messages.append({"role": "assistant", "content": assistant_response}) | |
| # Add the current user message | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| # Stream the response from the model | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the Gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox( | |
| value="""You are tasked with labeling text data based on both emotion temperature and text type categories. The final output must be a 13-character code that consists of the following structure: | |
| 1. Emotion Temperature Code (2 characters): | |
| - If the emotion is purely Cold: Use CC | |
| - If the emotion is purely Warm: Use WW | |
| - If the emotion is purely Hot: Use HH | |
| - If the emotion is a mix, use one of the following: | |
| - Cold and Warm: Use CW | |
| - Warm and Hot: Use WH | |
| - Cold and Hot: Use CH | |
| 2. Text Type Codes (next 9 digits): | |
| Assign a digit for each of the following categories based on the presence in the text. Use 0 for categories not applicable: | |
| 1: Toxic | |
| 2: Appreciation | |
| 3: Constructive Criticism | |
| 4: Genuine Questions | |
| 5: Advice/Suggestions | |
| 6: Requests | |
| 7: Spam | |
| 8: Off-Topic | |
| 9: Engagement Boosters | |
| 3. Special Categories (last 2 digits): | |
| If the text is Neutral/General: Set the 10th digit to 1; otherwise, set it to 0. | |
| If the text contains Hate: Set the last digit (11th) to 1; otherwise, set it to 0. | |
| Example: | |
| For the text "I love your videos but still something is missing": | |
| - Emotion: Cold and Warm (CW) | |
| - Types Detected: 2 (Appreciation), 3 (Constructive Criticism), 5 (Advice/Suggestions) | |
| - Special Categories: Neutral/General (set the 10th digit to 1), no Hate | |
| The output would be: CW02305000010 | |
| Output Format: | |
| Always return a 13-character code following this structure.""", | |
| label="each index of 13 digit have 0 to 9 , you need to extract the 13 digit number from the user input", | |
| lines=10, | |
| ), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |