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import asyncio
import json
import re
from datetime import datetime
from utils_evaluate import evaluate_answers, evaluate_objections
from utils_prep import offer_initial_actions
async def display_llm_responses(cl, session_state):
output = f"**Responses**"
await cl.Message(content=output).send()
for query, response in zip(session_state.queries, session_state.llm_responses):
query_display = {
"command": query["command"],
"message": query["message"],
"mood_score": query["mood_score"],
"previous_question": query["previous_question"],
"rep_answer": query["rep_answer"],
"next_question": query["next_question"],
}
query_json = json.dumps(query_display, indent=2)
await cl.Message(content="Query:").send()
await cl.Message(content=query_json).send()
await cl.Message(content="Response:").send()
await cl.Message(content=response).send()
remaining_queries = session_state.queries[len(session_state.llm_responses):]
remaining_responses = session_state.llm_responses[len(session_state.queries):]
for query in remaining_queries:
await cl.Message(content=f"**Query:** {query}").send()
for response in remaining_responses:
await cl.Message(content=f"**Response:** {response}").send()
def format_score(score):
if isinstance(score, (int, float)):
return f"{score*100:.1f}%"
return score
def format_rogue_score(score):
if isinstance(score, str):
match = re.search(r'precision=([\d.]+), recall=([\d.]+), fmeasure=([\d.]+)', score)
if match:
precision = float(match.group(1))
recall = float(match.group(2))
fmeasure = float(match.group(3))
return f"Precision: {precision*100:.1f}%, Recall: {recall*100:.1f}%, FMeasure: {fmeasure*100:.1f}%"
else:
precision = score.precision
recall = score.recall
fmeasure = score.fmeasure
return f"Precision: {precision*100:.1f}%, Recall: {recall*100:.1f}%, FMeasure: {fmeasure*100:.1f}%"
return score #
def format_datetime(dt):
if isinstance(dt, datetime):
return dt.strftime("%Y-%m-%d %H:%M")
return str(dt) #
async def display_evaluation_results(cl, session_state):
out_text = "*Preparing evaluation results ...*"
await cl.Message(content=out_text).send()
print("Checking evaluation and objection flags")
print(session_state.do_evaluation)
print(session_state.add_objections_to_analysis)
hit_miss_ratio = "N/A"
hit_miss_score = 0
if session_state.do_evaluation:
evaluate_answers(session_state)
elif session_state.add_objections_to_analysis:
await evaluate_objections(session_state)
for resp in session_state.responses:
if resp.get('evaluation_score', 'N/A') == 1 or resp.get('evaluation_score', 'N/A') == 0:
hit_miss = resp.get('evaluation_score', 0)
hit_miss_score += hit_miss
hit_miss_ratio = (hit_miss_score / len(session_state.responses)) * 100
await asyncio.sleep(1)
output = f"**Session Summary**"
await cl.Message(content=output).send()
output = f"**Start Time:** {format_datetime(session_state.start_time)} \n"
output = output + f"**End Time:** {format_datetime(session_state.end_time)} \n"
output = output + f"**Duration:** {session_state.duration_minutes} minutes \n"
output = output + f"**Total Number of Questions:** {len(session_state.questions)} \n"
output = output + f"**Total Questions Answered:** {len(session_state.responses)} \n"
await cl.Message(content=output).send()
if session_state.do_ragas_evaluation:
results_df = session_state.ragas_results.to_pandas()
columns_to_average = ['answer_relevancy', 'answer_correctness']
averages = results_df[columns_to_average].mean()
await cl.Message(content="**Overall Summary (By SalesBuddy)**").send()
output = f"**SalesBuddy Grade (1-10):** {session_state.responses[-1]['overall_score']} \n"
output = output + f"**SalesBuddy Evaluation:** {session_state.responses[-1]['overall_evaluation']} \n"
output = output + f"**SalesBuddy Final Mood Score (1-10):** {session_state.responses[-1]['mood_score']} \n"
output = output + f"**Hit/Miss Ratio:** {hit_miss_ratio:.1f}% \n"
await cl.Message(content=output).send()
if session_state.do_ragas_evaluation:
await cl.Message(content="**Average Scores - Based on RAGAS**").send()
output = "Answer Relevancy: " + str(format_score(averages['answer_relevancy'])) + "\n"
output = output + "Answer Correctness: " + str(format_score(averages['answer_correctness'])) + "\n"
await cl.Message(content=output).send()
await cl.Message(content="**Individual Question Scores**").send()
for index, resp in enumerate(session_state.responses):
eval_score = resp.get('evaluation_score', 0)
print(eval_score)
print(type(eval_score))
if eval_score == 1:
eval_output = "Hit"
elif eval_score == 0:
eval_output = "Miss"
else:
eval_output = "N/A"
output = f"""
**Question:** {resp.get('question', 'N/A')}
**Answer:** {resp.get('response', 'N/A')}
**SalesBuddy Evaluation:** {resp.get('response_evaluation', 'N/A')}
**Hit/Miss:** {eval_output}
"""
if session_state.do_ragas_evaluation:
scores = session_state.scores[index]
relevancy = scores.get('answer_relevancy', 'N/A')
correctness = scores.get('answer_correctness', 'N/A')
bleu_score = scores.get('bleu_score', 'N/A')
rouge1_score = scores.get('rouge_score', {}).get('rouge1', 'N/A')
rouge1_output = format_rogue_score(rouge1_score)
rougeL_score = scores.get('rouge_score', {}).get('rougeL', 'N/A')
rougeL_output = format_rogue_score(rougeL_score)
semantic_similarity_score = scores.get('semantic_similarity_score', 'N/A')
numbers = f"""
**Answer Relevancy:** {format_score(relevancy)}
**Answer Correctness:** {format_score(correctness)}
**BLEU Score:** {format_score(bleu_score)}
**ROUGE 1 Score:** {rouge1_output}
**ROUGE L Score:** {rougeL_output}
**Semantic Similarity Score:** {format_score(semantic_similarity_score)}
"""
await cl.Message(content=output).send()
await cl.Message(content=numbers).send()
else:
await cl.Message(content=output).send()
await offer_initial_actions() |