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| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # import pandas as pd | |
| # """ | |
| # 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") | |
| # ################################################################ | |
| # # Load your CSV file | |
| # df = pd.read_csv("your_file.csv") | |
| # # Create dropdowns for exam name, year, and problem number | |
| # exam_names = df["exam name"].unique() | |
| # year_options = df["year"].unique() | |
| # problem_numbers = df["problem number"].unique() | |
| # exam_dropdown = gr.Dropdown(exam_names, label="Exam Name") | |
| # year_dropdown = gr.Dropdown(year_options, label="Year") | |
| # problem_dropdown = gr.Dropdown(problem_numbers, label="Problem Number") | |
| # # Define the functions for the three buttons | |
| # def solve_problem(exam, year, problem): | |
| # problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0] | |
| # prompt = f"Solve the following problem: {problem_statement}" | |
| # response = client.chat_completion(prompt, max_tokens=512, temperature=0.7, top_p=0.95) | |
| # return response.choices[0].text | |
| # def give_hints(exam, year, problem): | |
| # problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0] | |
| # prompt = f"Give hints for the following problem: {problem_statement}" | |
| # response = client.chat_completion(prompt, max_tokens=512, temperature=0.7, top_p=0.95) | |
| # return response.choices[0].text | |
| # def create_similar_problem(exam, year, problem): | |
| # problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0] | |
| # prompt = f"Create a similar problem to the following one: {problem_statement}" | |
| # response = client.chat_completion(prompt, max_tokens=512, temperature=0.7, top_p=0.95) | |
| # return response.choices[0].text | |
| # ################################################################ | |
| # def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| # ): | |
| # 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]}) | |
| # messages.append({"role": "user", "content": message}) | |
| # 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 | |
| # """ | |
| # 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 friendly Chatbot.", 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)", | |
| # ), | |
| # ], | |
| # ) | |
| # ################################################################ | |
| # # Create Gradio interface with Blocks context | |
| # with gr.Blocks() as dropdown_interface: | |
| # with gr.Column(): | |
| # exam_dropdown.render() | |
| # year_dropdown.render() | |
| # problem_dropdown.render() | |
| # solve_button = gr.Button("Solve Problem") | |
| # hints_button = gr.Button("Give Hints") | |
| # similar_problem_button = gr.Button("Create Similar Problem") | |
| # output_text = gr.Textbox(label="Output") | |
| # solve_button.click(solve_problem, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text) | |
| # hints_button.click(give_hints, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text) | |
| # similar_problem_button.click(create_similar_problem, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text) | |
| # ################################################################ | |
| # # Combine both interfaces into a tabbed layout | |
| # tabbed_interface = gr.TabbedInterface( | |
| # [dropdown_interface, demo], | |
| # ["Problem Solver", "Chat Interface"] | |
| # ) | |
| # ################################################################ | |
| # # Launch the app | |
| # if __name__ == "__main__": | |
| # tabbed_interface.launch() | |
| import pandas as pd | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| # Initialize the InferenceClient | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # Load your CSV file | |
| df = pd.read_csv("your_file.csv") | |
| # Create dropdowns for exam name, year, and problem number | |
| exam_names = df["exam name"].unique() | |
| year_options = df["year"].unique() | |
| problem_numbers = df["problem number"].unique() | |
| exam_dropdown = gr.Dropdown(exam_names, label="Exam Name") | |
| year_dropdown = gr.Dropdown(year_options, label="Year") | |
| problem_dropdown = gr.Dropdown(problem_numbers, label="Problem Number") | |
| # Define the functions for the three buttons | |
| def solve_problem(exam, year, problem): | |
| problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0] | |
| prompt = f"Solve the following problem: {problem_statement}" | |
| response = client.text_generation(prompt, max_new_tokens=512, temperature=0.7, top_p=0.95, model = "HuggingFaceH4/zephyr-7b-beta") | |
| return response[0]['generated_text'] | |
| def give_hints(exam, year, problem): | |
| problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0] | |
| prompt = f"Give hints for the following problem: {problem_statement}" | |
| response = client.text_generation(prompt, max_new_tokens=512, temperature=0.7, top_p=0.95, model = "HuggingFaceH4/zephyr-7b-beta") | |
| return response[0]['generated_text'] | |
| def create_similar_problem(exam, year, problem): | |
| problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem statement"].values[0] | |
| prompt = f"Create a similar problem to the following one: {problem_statement}" | |
| response = client.text_generation(prompt, max_new_tokens=512, temperature=0.7, top_p=0.95, model = "HuggingFaceH4/zephyr-7b-beta") | |
| return response[0]['generated_text'] | |
| # Define the chat response function | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| 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]}) | |
| messages.append({"role": "user", "content": message}) | |
| 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 | |
| # Create Gradio interface with Blocks context | |
| with gr.Blocks() as dropdown_interface: | |
| with gr.Column(): | |
| exam_dropdown.render() | |
| year_dropdown.render() | |
| problem_dropdown.render() | |
| solve_button = gr.Button("Solve Problem") | |
| hints_button = gr.Button("Give Hints") | |
| similar_problem_button = gr.Button("Create Similar Problem") | |
| output_text = gr.Textbox(label="Output") | |
| solve_button.click(solve_problem, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text) | |
| hints_button.click(give_hints, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text) | |
| similar_problem_button.click(create_similar_problem, inputs=[exam_dropdown, year_dropdown, problem_dropdown], outputs=output_text) | |
| chat_interface = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot.", 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)", | |
| ), | |
| ], | |
| ) | |
| # Combine both interfaces into a tabbed layout | |
| tabbed_interface = gr.TabbedInterface( | |
| [dropdown_interface, chat_interface], | |
| ["Problem Solver", "Chat Interface"] | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| tabbed_interface.launch() | |