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kowshik
2025-01-31 15:59:51 +00:00
parent 8ce331b023
commit 1a68b4407e
14 changed files with 5436 additions and 274 deletions
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src/services/chatbot.py
+106 -22
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@@ -9,9 +9,70 @@ from src.models.sop_response_schemas import *
from src.models.bot_response_schema import *
from scripts.assessment_data import generate_summary_stats_v2
from dotenv import load_dotenv
from scripts.statistics_data import (generate_summary_stats,create_json_body,fetch_data)
load_dotenv()
import random
import json
from datetime import datetime, timedelta
import pandas as pd
# Define possible areas
areas = [
"Agile Methodologies", "API Development", "Code Review", "Collaboration with Cross-Functional Teams",
"Continuous Integration/Continuous Deployment (CI/CD)", "Debugging Techniques", "Documentation",
"Performance Optimization", "System Design", "Unit Testing", "Version Control"
]
# Generate random dates
def random_date():
start_date = datetime(2024, 1, 1)
return (start_date + timedelta(days=random.randint(1, 300))).strftime('%Y-%m-%dT00:00:00.000Z')
# Generate dummy assessments
def generate_dummy_data(num_assessments=50, max_users_per_assessment=5):
data = []
for i in range(1, num_assessments + 1):
assessment_id = str(i)
assessment_name = f"Assessment {i}"
start_date = random_date()
red_flags = random.randint(0, 5)
open_items = random.randint(0, 50)
completed_items = random.randint(0, 50)
total_assigned_items = open_items + completed_items
# Generate random users for each assessment
user_details = []
num_users = random.randint(1, max_users_per_assessment)
for _ in range(num_users):
user_name = f"User_{random.randint(1, 100)}"
user_total_items = random.randint(1, 50)
user_completed_items = random.randint(0, user_total_items)
area_list = random.sample(areas, random.randint(1, 5))
user_details.append({
"name": user_name,
"total_assigned_items": user_total_items,
"completed_items": user_completed_items,
"area_list": area_list
})
data.append({
"assessment_id": assessment_id,
"red_flags": red_flags,
"open_items": open_items,
"completed_items": completed_items,
"total_assigned_items": total_assigned_items,
"assessment_name": assessment_name,
"start_date": start_date,
"user_details": user_details
})
return {"error": False, "data": data}
# Generate dummy data
dummy_data = generate_dummy_data(num_assessments=100, max_users_per_assessment=5)
#print(dummy_data)
#SopGeneratorDocument
class Chatbot:
def __init__(self):
@@ -123,10 +184,14 @@ class Chatbot:
"""
try:
# Define the path to the company's assessment data (stored as a CSV)
data_path = os.path.join('data', 'raw', 'erp_company_assessment', f'{companyid}_raw_data.csv')
# Generate summary statistics from the company's assessment data
summary_stats = generate_summary_stats_v2(file_path=data_path)
json_body_area = create_json_body("problematic-areas", companyid)
json_body_assessment = create_json_body("user-stats-by-assessment", companyid)
# Fetching data
problematic_areas_data = fetch_data(json_body_area)
assessment_data = fetch_data(json_body_assessment)
summary_stats = generate_summary_stats(assessment_data, problematic_areas_data)
print(summary_stats)
# Generate the prompt using the company info and the summary statistics
@@ -164,7 +229,7 @@ class Chatbot:
return None
def predict_next_n_assessment(self, company_info, companyid, N) -> AssessmentPredictionsResponse:
def predict_next_n_assessment(self,companyid, N) -> AssessmentPredictionsResponse:
"""
This method generates predictions based on past assessment data of a company. It queries the backend for the
company's assessment data, generates a prompt, and then uses the GPT-4 model to return predictions based on the query.
@@ -176,51 +241,62 @@ class Chatbot:
:return: Result containing the prediction result or None if an error occurs.
"""
try:
json_body_area = create_json_body("problematic-areas", companyid)
json_body_assessment = create_json_body("user-stats-by-assessment", companyid)
# Fetching data
problematic_areas_data = fetch_data(json_body_area)
assessment_data = fetch_data(json_body_assessment)
summary_stats = generate_summary_stats(assessment_data, problematic_areas_data)
print(summary_stats)
# Define the path to the company's assessment data (stored as a CSV)
data_path = os.path.join('data', 'raw', 'erp_company_assessment', f'{companyid}_raw_data.csv')
#data_path = os.path.join('data', 'raw', 'erp_company_assessment', f'{companyid}_raw_data.csv')
# Generate summary statistics from the company's assessment data
summary_stats = generate_summary_stats_v2(file_path=data_path)
#summary_stats = generate_summary_stats_v2(file_path=data_path)
# Generate the prompt using the company info and the summary statistics
prompt = predict_next_n_assessments_prompt()
prompt = predict_next_n_assessments_prompt_v2()
# Interact with GPT-4 model to get a response
MODEL = "gpt-4o"
response = self.client.beta.chat.completions.parse(
model=self.model,
model=MODEL,
messages=[
{
"role": "system",
"content": f"{prompt}"
},
{
"role": "user",
"content": f"company info: {company_info}--> N-value is {N} ",
"content": f"Summary stattistics of company past assessment: {summary_stats}",
},
{
{
"role": "user",
"content": f"Summary stats: {summary_stats}",
"content": f"Number of next assessments to predict: {N}",
}
],
response_format=AssessmentPredictionsResponse,
#response_format=AssessmentPredictionsResponse,
max_tokens=1024,
temperature=0.1
)
# Extract the response from the GPT-4 model
extracted_text = json.loads(response.choices[0].message.content)
preds = json.loads(response.choices[0].message.content)
# Initialize dictionary to store assessments with dynamic names
predictions = {}
#predictions = {}
# Loop through the predicted assessments and rename them dynamically
for i in range(N):
assessment_key = f"assessment_{i + 1}"
predictions[assessment_key] = extracted_text["predictions"][i]['AssessmentN']
#for i in range(N):
#assessment_key = f"assessment_{i + 1}"
#predictions[assessment_key] = extracted_text["predictions"][i]['AssessmentN']
# Return the dynamically named assessments
return predictions
return preds
except Exception as e:
print(f"An error occurred: {e}")
@@ -279,10 +355,18 @@ class Chatbot:
"""
try:
# Define the path to the company's assessment data (stored as a CSV)
data_path = os.path.join('data', 'raw', 'erp_company_assessment', f'{companyid}_raw_data.csv')
#data_path = os.path.join('data', 'raw', 'erp_company_assessment', f'{companyid}_raw_data.csv')
# Generate summary statistics from the company's assessment data
summary_stats = generate_summary_stats_v2(file_path=data_path)
#summary_stats = generate_summary_stats_v2(file_path=data_path)
json_body_area = create_json_body("problematic-areas", companyid)
json_body_assessment = create_json_body("user-stats-by-assessment", companyid)
# Fetching data
problematic_areas_data = fetch_data(json_body_area)
assessment_data = fetch_data(json_body_assessment)
summary_stats = generate_summary_stats(assessment_data, problematic_areas_data)
# Generate the prompt using the company info and the summary statistics
prompt = predict_goal_achievement_probability_prompt()
@@ -301,7 +385,7 @@ class Chatbot:
},
{
"role": "user",
"content": f"Summary stats: {summary_stats}",
"content": f"Summary stats of past assement: {summary_stats}",
}
],
response_format=AchievementPrediction,