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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) |