Update llm_part.py

llm_part.py

@@ -1,100 +1,99 @@
-# 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 secret_key import gemeni_key, llama_key
-from datetime import datetime
-
-# Initialize LLM models
-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]  
-
-
-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
+# 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
+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]  
+
+
+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