Update chain_recommendations.py

chain_recommendations.py

@@ -1,32 +1,29 @@
+# chain_recommendations.py
 import json
 from typing import Dict
 from langchain import PromptTemplate, LLMChain
 from models import chat_model
 
-# Enhanced Prompt Template
-recommend_prompt_template = PromptTemplate(
+improved_recommend_prompt_template = PromptTemplate(
     input_variables=["problems"],
     template=(
-        "You are a helpful wellness recommendation system. Given the following problem severity percentages:\n"
+        "You are a wellness recommendation assistant. Given the following problem severity percentages:\n"
         "{problems}\n\n"
-        "Based on these strict rules:\n"
-        "- If sleep_problem > 70: Recommend Sleep Improvement Package\n"
-        "- If stress_problem > 70: Recommend Stress Reduction Package\n"
-        "- If exercise_problem > 70: Recommend Exercise Enhancement Package\n"
-        "- If all problems are between 30 and 70: Recommend Balanced Wellness Package\n"
-        "- If no severe problems: Recommend General Wellness Package\n\n"
-        "List the recommended wellness packages separated by commas. "
-        "Do not include any additional text or explanation."
+        "Carefully analyze these percentages and consider nuanced differences between the areas. "
+        "Your goal is to recommend the most appropriate wellness packages based on a detailed assessment of these numbers, "
+        "not just fixed thresholds. Consider the following guidelines:\n\n"
+        "- If one area is extremely high (above 70) while others are lower, prioritize a package targeting that area.\n"
+        "- If multiple areas are high or near high (e.g., above 60), consider recommending multiple specialized packages or a comprehensive program.\n"
+        "- If all areas are moderate (between 30 and 70), recommend a balanced wellness package that addresses overall health.\n"
+        "- If all areas are low, a general wellness package might be sufficient.\n"
+        "- Consider borderline cases and recommend packages that address both current issues and preventive measures.\n\n"
+        "Return the recommended wellness packages in a JSON array format."
     )
 )
 
-recommend_chain = LLMChain(llm=chat_model, prompt=recommend_prompt_template)
+# Initialize the improved recommendation chain
+recommend_chain = LLMChain(llm=chat_model, prompt=improved_recommend_prompt_template)
 
 def generate_recommendations(problems: Dict[str, float]) -> str:
-    """
-    Generates wellness package recommendations based on problem severity percentages.
-    The function accepts a dictionary of problem severities and returns a 
-    comma-separated string of recommended packages based on predefined rules.
-    """
     recommendations = recommend_chain.run(problems=json.dumps(problems))
     return recommendations.strip()