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| import os | |
| import time | |
| import gradio as gr | |
| import pandas as pd | |
| from model import Model | |
| from tqdm import tqdm | |
| from gradio_pdf import PDF | |
| tqdm.pandas() | |
| OUTPUT_FILE = "./results_qa.csv" | |
| TOC_FILE = "./TOC.pdf" | |
| def new_vote(data: gr.LikeData, question, model_name, **kwargs): | |
| feedback = "Good" if data.liked else "Bad" | |
| df = pd.read_csv(OUTPUT_FILE) | |
| df['Feedback'] = df.apply(lambda x: feedback if (x.Model == model_name and x.Question == question) else None, axis = 1) | |
| df.to_csv(OUTPUT_FILE, index=False) | |
| # def answer_question(question: str, model_name: str, system_prompt: str): | |
| # start_time = time.time() | |
| # qa_model = Model(model_name=model_name) | |
| # response, sources = qa_model.run(system_prompt=system_prompt, query=question) | |
| # time_taken = time.time() - start_time | |
| # words = len(question) + len(response) | |
| # efficiency = words / time_taken | |
| # final_response = f"{response} \n\nTime Taken: {time_taken}" | |
| # new_row = {'Model': model_name, 'Question': question, 'Answer': response, "Sources": sources, "Time": time_taken, "Words": words, "Efficiency": efficiency, "Feedback": None, "final_response": final_response} | |
| # if os.path.isfile(OUTPUT_FILE): | |
| # df = pd.read_csv(OUTPUT_FILE) | |
| # rows = df.values.tolist() | |
| # # print("df.values.tolist(): ", df.values.tolist()) | |
| # # df = df.append(new_row, ignore_index=True) | |
| # rows.append(new_row) | |
| # else: | |
| # rows = [new_row] | |
| # df = pd.DataFrame(rows) | |
| # df.to_csv(OUTPUT_FILE, index=False) | |
| # yield [(question, final_response)] | |
| def answer_question(question: str, model_name: str, system_prompt: str): | |
| start_time = time.time() | |
| qa_model = Model(model_name=model_name) | |
| gen_response = qa_model.run(system_prompt=system_prompt, query=question) | |
| response = "" | |
| for resp in gen_response: | |
| if isinstance(resp, list): | |
| sources = resp | |
| break | |
| resp = resp.replace("$", "₹") | |
| response += resp | |
| yield [(question, response)], OUTPUT_FILE | |
| time_taken = time.time() - start_time | |
| words = len(question) + len(response) | |
| efficiency = words / time_taken | |
| temp_sources = "\n".join([f"{i + 1}. {d}" for i, d in enumerate(sources)]) | |
| final_response = f"{response} \n\nSources: \n{temp_sources} \n\nTime Taken: {time_taken}" | |
| new_row = {'Model': model_name, 'Question': question, 'Answer': response, "Sources": sources, "Time": time_taken, "Words": words, "Efficiency": efficiency, "Feedback": None, "final_response": final_response} | |
| if os.path.isfile(OUTPUT_FILE): | |
| try: | |
| df = pd.read_csv(OUTPUT_FILE) | |
| rows = df.to_dict(orient="records") | |
| rows.append(new_row) | |
| except Exception: | |
| rows = [new_row] | |
| else: | |
| rows = [new_row] | |
| df = pd.DataFrame(rows) | |
| df.to_csv(OUTPUT_FILE, index=False) | |
| final_response = final_response.strip("Question").strip("\n") | |
| final_response = final_response.strip("\n").strip(" ").strip("Answer:").strip("Question").strip("\n").replace("Answer:", "") | |
| yield [(question, final_response)], OUTPUT_FILE | |
| if __name__ == "__main__": | |
| with gr.Blocks() as demo: | |
| chatbot = gr.Chatbot() | |
| # system_prompt = """Answer the question using the context. Provide examples only from the context and use only Rupees (₹) in examples. If you don't know the answer, just say 'Please rephrase the question I am unable to answer'""" | |
| system_prompt = """"Answer the question using the context. Provide examples only from the context and use only Rupees (₹) in examples. If you don't know the answer, just say 'Please rephrase the question I am unable to answer'""" | |
| system_prompt = "Use the following pieces of book to answer the question at the end. \nIf you don't know the answer, please think rationally and answer from the book" | |
| system_prompt = """Answer the question using the context. Provide examples only from the context and use only Rupees (₹) in examples. If you don't know the answer, just say 'Please rephrase the question I am unable to answer'""" | |
| system_prompt = """Answer the question from the book. Provide examples only from the book. If you don't know the answer, just say 'Please rephrase the question'""" | |
| choices=["gpt4", "gpt-3.5-turbo"] | |
| system_prompt = gr.Textbox(value=system_prompt, label="System Prompt") | |
| textbox = gr.Textbox(label="Query") | |
| model_name = gr.Dropdown(choices=choices, value="gpt-3.5-turbo", label="Model") | |
| pdf = PDF(interactive=True, value=TOC_FILE, label="Tabel of Contents") | |
| file = gr.File(value = OUTPUT_FILE, file_types=["csv"], label="Output") | |
| textbox.submit(answer_question, [textbox, model_name, system_prompt], [chatbot, file]) | |
| chatbot.like(new_vote, [textbox, model_name], None) | |
| demo.queue() | |
| demo.launch(share=True) |