Spaces:
Sleeping
Sleeping
File size: 4,554 Bytes
429a64c 72b325b 429a64c 72b325b 429a64c 216d651 2834dcd 429a64c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
# llm_part.py
import os
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from datetime import datetime
# Initialize LLM models
gemeni_key=os.getenv("gemeni_key")
llama_key=os.getenv("llama_key")
llm_1 = ChatGoogleGenerativeAI(model="gemini-pro", api_key=gemeni_key)
llm_2 = ChatGroq(model="llama3-groq-70b-8192-tool-use-preview", groq_api_key=llama_key)
# Prayer times for the current day only (Fajr, Dhuhr, Asr, Maghrib, Isha)
prayer_times = {
"Fajr": "04:30",
"Dhuhr": "11:45",
"Asr": "15:30",
"Maghrib": "17:45",
"Isha": "19:00"
}
# Define the next prayer function
def get_next_prayer(current_time):
# Retrieve today's prayer times
times = list(prayer_times.values())
prayer_names = list(prayer_times.keys())
# Current time in comparable format
current_hour = current_time.hour
current_minute = current_time.minute
for prayer, time_str in zip(prayer_names, times):
prayer_hour, prayer_minute = map(int, time_str.split(':'))
if (prayer_hour > current_hour) or (prayer_hour == current_hour and prayer_minute > current_minute):
return prayer, time_str # Return both prayer name and time
#return "Isha", times[-1]
return prayer_times, times[-1]
templates = {
"Learning": "You just completed a learning task: {task_name}. You studied for {hours} hours and {minutes} minutes. "
"Can you suggest improvements to my learning process? What should I learn next?",
"Gym": "You completed a gym task: {task_name}. It lasted {hours} hours and {minutes} minutes. "
"How can I optimize my workout? What should I focus on next time in the gym?",
"Personal": "I just completed a personal task: {task_name}. It took me {hours} hours and {minutes} minutes. "
"What advice can you give for better time management or improvement?",
"Work": "I completed a work-related task: {task_name}, which lasted {hours} hours and {minutes} minutes. "
"Can you suggest ways to improve my work efficiency? What should I work on next?",
"Prayer": "You completed a prayer task: {task_name}. It took you {hours} hours and {minutes} minutes. "
"What practical steps can I take to deepen my spiritual connection during Salah (prayer)?"
"Please provide specific traditions and teachings for the next prayer, **{next_prayer}**, which is at **{next_time}**. "
"Format your response as bullet points for clarity."
}
# Function to select a template based on the task category
def get_template(category):
return templates.get(category, "You just completed the task: {task_name}. It took {hours} hours and {minutes} minutes. "
"What advice can you give? What is the next most productive task?")
# Template function to generate advice and next task recommendations for each task
def get_task_advice(task_name, task_category, hours, minutes):
# Get the template based on the category
template_str = get_template(task_category)
# Determine the next prayer and its time
current_time = datetime.now()
next_prayer, next_time = get_next_prayer(current_time)
# Create the prompt
prompt_template = PromptTemplate(
input_variables=["task_name", "hours", "minutes", "next_prayer", "next_time"],
template=template_str
)
prompt = prompt_template.format(task_name=task_name, hours=hours, minutes=minutes,
next_prayer=next_prayer, next_time=next_time)
# Set up LLMChain for both models
chain_1 = LLMChain(llm=llm_1, prompt=prompt_template)
chain_2 = LLMChain(llm=llm_2, prompt=prompt_template)
try:
# Run the LLMChain to get the responses
response_llm1 = chain_1.run({"task_name": task_name, "hours": hours, "minutes": minutes,
"next_prayer": next_prayer, "next_time": next_time})
except Exception as e:
response_llm1 = f"Error generating response from Gemini: {str(e)}"
try:
response_llm2 = chain_2.run({"task_name": task_name, "hours": hours, "minutes": minutes,
"next_prayer": next_prayer, "next_time": next_time})
except Exception as e:
response_llm2 = f"Error generating response from Llama: {str(e)}"
return response_llm1, response_llm2
|