import os import time import gradio as gr import pandas as pd from model import Model from tqdm import tqdm tqdm.pandas() OUTPUT_FILE = "./results_qa.csv" 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() # with gr.Row(): textbox = gr.Textbox(label="Query") # 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") model_name = gr.Dropdown(choices=choices, value="gpt-3.5-turbo", label="Model") 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)