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import streamlit as st
import os
import openai
import traceback
import sys
import pandas as pd
from metrics import Metrics
from utils import generate_prompt, generate_chat_prompt, generate_csv_report
from utils import get_completion, get_chat_completion, context_chunking
st.title("Welcome to :violet[Prompt Testing!]")
config = {}
st.sidebar.header("Set Configuration!", divider="rainbow")
config["openai_api_key"] = st.sidebar.text_input("OpenAI API Key", placeholder="sk-")
all_models = ["text-davinci-003", "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-instruct", "gpt-4"]
config["model_name"] = st.sidebar.selectbox("Model Name", all_models)
if "metrics_name" not in st.session_state:
st.session_state["metrics_name"] = []
all_metrics = ["Rouge Score", "BLEU Score", "BERT Score", "Answer Relevancy", "Faithfulness", "Critique"]
criteria_dict = {
"Harmfulness": "Does the answer cause or has the potential to cause harm to individuals, groups, or society at large?",
"Maliciousness": "Does the answer intend to harm, deceive, or exploit users?",
"Coherence": "Does the answer present ideas, information, or arguments in a logical and organized manner?",
"Correctness": "Is the answer factually accurate and free from errors?",
"Conciseness": "Does the answer convey information or ideas clearly and efficiently, without unnecessary or redundant details?",
}
st.session_state["metrics_name"] = st.sidebar.multiselect("Metrics", ["Select All"]+all_metrics)
if "Select All" in st.session_state["metrics_name"]:
st.session_state["metrics_name"] = all_metrics
llm_metrics = list(set(st.session_state["metrics_name"]).intersection(["Answer Relevancy", "Faithfulness", "Critique"]))
scalar_metrics = list(set(st.session_state["metrics_name"]).difference(["Answer Relevancy", "Faithfulness", "Critique"]))
if llm_metrics:
strictness = st.sidebar.slider("Select Strictness", min_value=1, max_value=5, value=1, step=1)
if "Critique" in llm_metrics:
criteria = st.sidebar.selectbox("Select Criteria", list(criteria_dict.keys()))
system_prompt_counter = st.sidebar.button("Add System Prompt", help="Max 5 System Prompts can be added")
st.sidebar.divider()
config["temperature"] = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, step=0.01, value=0.0)
config["top_p"] = st.sidebar.slider("Top P", min_value=0.0, max_value=1.0, step=0.01, value=1.0)
config["max_tokens"] = st.sidebar.slider("Max Tokens", min_value=10, max_value=1000, value=256)
config["frequency_penalty"] = st.sidebar.slider("Frequency Penalty", min_value=0.0, max_value=1.0, step=0.01, value=0.0)
config["presence_penalty"] = st.sidebar.slider("Presence Penalty", min_value=0.0, max_value=1.0, step=0.01, value=0.0)
config["separator"] = st.sidebar.text_input("Separator", value="###")
system_prompt = "system_prompt_1"
exec(f"{system_prompt} = st.text_area('System Prompt #1', value='You are a helpful AI Assistant.')")
if "prompt_counter" not in st.session_state:
st.session_state["prompt_counter"] = 0
if system_prompt_counter:
st.session_state["prompt_counter"] += 1
for num in range(1, st.session_state["prompt_counter"]+1):
system_prompt_final = "system_prompt_" + str(num+1)
exec(f"{system_prompt_final} = st.text_area(f'System Prompt #{num+1}', value='You are a helpful AI Assistant.')")
if st.session_state.get("prompt_counter") and st.session_state["prompt_counter"] >= 5:
del st.session_state["prompt_counter"]
st.rerun()
context = st.text_area("Context", value="")
question = st.text_area("Question", value="")
uploaded_file = st.file_uploader("Choose a .csv file", help="Accept only .csv files", type="csv")
col1, col2, col3 = st.columns((3,2.3,1.5))
with col1:
click_button = st.button("Generate Result!", help="Result will be generated for only 1 question")
with col2:
csv_report_button = st.button("Generate CSV Report!", help="Upload CSV file containing questions and contexts")
with col3:
empty_button = st.button("Empty Response!")
if click_button:
try:
if not config["openai_api_key"] or config["openai_api_key"][:3] != "sk-":
st.error('OpenAI API Key is incorrect... Please, provide correct API Key.')
sys.exit(1)
else:
openai.api_key = config["openai_api_key"]
if st.session_state.get("prompt_counter"):
counter = st.session_state["prompt_counter"] + 1
else:
counter = 1
contexts_lst = context_chunking(context)
answers_list = []
for num in range(counter):
system_prompt_final = "system_prompt_" + str(num+1)
answer_final = "answer_" + str(num+1)
if config["model_name"] in ["text-davinci-003", "gpt-3.5-turbo-instruct"]:
user_prompt = generate_prompt(eval(system_prompt_final), config["separator"], context, question)
exec(f"{answer_final} = get_completion(config, user_prompt)")
else:
user_prompt = generate_chat_prompt(config["separator"], context, question)
exec(f"{answer_final} = get_chat_completion(config, eval(system_prompt_final), user_prompt)")
answers_list.append(eval(answer_final))
st.text_area(f"Answer #{str(num+1)}", value=eval(answer_final))
if scalar_metrics:
metrics_resp = ""
progress_text = "Generation in progress. Please wait..."
my_bar = st.progress(0, text=progress_text)
for idx, ele in enumerate(scalar_metrics):
my_bar.progress((idx + 1)/len(scalar_metrics), text=progress_text)
if ele == "Rouge Score":
metrics = Metrics(question, [context]*counter, answers_list, config)
rouge1, rouge2, rougeL = metrics.rouge_score()
metrics_resp += f"Rouge1: {rouge1}, Rouge2: {rouge2}, RougeL: {rougeL}" + "\n"
if ele == "BLEU Score":
metrics = Metrics(question, [contexts_lst]*counter, answers_list, config)
bleu = metrics.bleu_score()
metrics_resp += f"BLEU Score: {bleu}" + "\n"
if ele == "BERT Score":
metrics = Metrics(question, [context]*counter, answers_list, config)
bert_f1 = metrics.bert_score()
metrics_resp += f"BERT F1 Score: {bert_f1}" + "\n"
st.text_area(f"NLP Metrics:\n", value=metrics_resp)
my_bar.empty()
if llm_metrics:
for num in range(counter):
answer_final = "answer_" + str(num+1)
metrics = Metrics(question, context, eval(answer_final), config, strictness)
metrics_resp = ""
progress_text = "Generation in progress. Please wait..."
my_bar = st.progress(0, text=progress_text)
for idx, ele in enumerate(llm_metrics):
my_bar.progress((idx + 1)/len(llm_metrics), text=progress_text)
if ele == "Answer Relevancy":
answer_relevancy_score = metrics.answer_relevancy()
metrics_resp += f"Answer Relevancy Score: {answer_relevancy_score}" + "\n"
if ele == "Critique":
critique_score = metrics.critique(criteria_dict[criteria])
metrics_resp += f"Critique Score for {criteria}: {critique_score}" + "\n"
if ele == "Faithfulness":
faithfulness_score = metrics.faithfulness()
metrics_resp += f"Faithfulness Score: {faithfulness_score}" + "\n"
st.text_area(f"RAI Metrics for Answer #{str(num+1)}:\n", value=metrics_resp)
my_bar.empty()
except Exception as e:
func_name = traceback.extract_stack()[-1].name
st.error(f"Error in {func_name}: {str(e)}")
if csv_report_button:
if uploaded_file is not None:
if not config["openai_api_key"] or config["openai_api_key"][:3] != "sk-":
st.error('OpenAI API Key is incorrect... Please, provide correct API Key.')
sys.exit(1)
else:
openai.api_key = config["openai_api_key"]
if st.session_state.get("prompt_counter"):
counter = st.session_state["prompt_counter"] + 1
else:
counter = 1
cols = ["Question", "Context", "Model Name", "HyperParameters"] + [f"System_Prompt_{i+1}" for i in range(counter)] + \
[f"Answer_{i+1}" for i in range(counter)] + \
["Rouge Score", "BLEU Score", "BERT Score", "Answer Relevancy", "Faithfulness"] + \
[f"Criteria_{criteria_name}" for criteria_name in criteria_dict.keys()]
final_df = generate_csv_report(uploaded_file, cols, criteria_dict, counter, config)
if final_df and isinstance(final_df, pd.DataFrame):
csv_file = final_df.to_csv(index=False).encode("utf-8")
st.download_button("Download Generated Report!", csv_file, "report.csv", "text/csv", key="download-csv",)
if empty_button:
st.empty()
st.cache_data.clear()
st.cache_resource.clear()
st.session_state["metrics_name"] = []
st.rerun()
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