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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") | |
| # Predefined list of interview questions | |
| interview_questions = [ | |
| "Can you tell me about yourself?", | |
| "Why are you interested in this position?", | |
| "What are your strengths and weaknesses?", | |
| "Can you describe a challenging work situation and how you handled it?", | |
| "Where do you see yourself in five years?", | |
| ] | |
| # Keep track of the current question index | |
| question_index = 0 | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| global question_index | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| # Add the user's latest message | |
| messages.append({"role": "user", "content": message}) | |
| # Generate the assistant's response | |
| response = "" | |
| 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 | |
| # Prepare the next question if there are more questions left | |
| if question_index < len(interview_questions): | |
| next_question = interview_questions[question_index] | |
| question_index += 1 | |
| else: | |
| next_question = "Thank you for answering all the questions. Do you have any questions for me?" | |
| # Append the response to the history and add the next question | |
| history.append((message, response)) | |
| yield response + "\n\n" + next_question | |
| """ | |
| 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 a manager conducting a job interview. Ask questions related to the candidate's experience, skills, and suitability for the role.", label="System message"), | |
| 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() | |