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# chain_recommendations.py
import json
from typing import Dict
from langchain import PromptTemplate, LLMChain
from models import chat_model
#
# UPDATED PROMPT:
# We ask the LLM to return:
# 1) A JSON array (on its own line) listing the recommended packages
# 2) Followed by detailed headings explaining WHY each recommended package is chosen
# and possibly why other packages are excluded.
#
improved_recommend_prompt_template = PromptTemplate(
input_variables=["problems"],
template=(
"You are a wellness recommendation assistant. You receive problem severity percentages:\n"
"{problems}\n\n"
"We have these potential packages:\n"
"1. Fitness & Mobility | Tagline: 'Enhance Mobility. Boost Fitness.'\n"
"2. No More Insomnia | Deep Rest | Tagline: 'Reclaim Your Sleep. Restore Your Mind.'\n"
"3. Focus Flow | Clarity Boost | Tagline: 'Stay Focused. Stay Productive.'\n"
"4. Boost Energy | Tagline: 'Fuel Your Day. Boost Your Energy.'\n"
"5. Chronic Care | Chronic Support | Tagline: 'Ongoing Support for Chronic Wellness.'\n"
"6. Mental Wellness | Calm Mind | Tagline: 'Find Peace of Mind, Every Day.'\n\n"
"Carefully analyze these percentages, considering:\n"
"- If one area is extremely high (above 70), prioritize that area.\n"
"- If multiple areas are high (above 60), recommend multiple specialized packages.\n"
"- If all areas are moderate (30 to 70), suggest a balanced approach.\n"
"- If all areas are low, a general or minimal package might suffice.\n"
"- Consider borderline and preventative measures.\n\n"
"## IMPORTANT:\n"
"1) First, output the recommended packages in a strict JSON array format, for example:\n"
" ```json\n"
" [\n"
" \"Mental Wellness\",\n"
" \"Fitness & Mobility\"\n"
" ]\n"
" ```\n"
" Each item must be exactly one of these valid names:\n"
" \"Fitness & Mobility\", \"No More Insomnia\", \"Focus Flow\", \"Boost Energy\", \"Chronic Care\", \"Mental Wellness\".\n\n"
"2) After that JSON array, provide explanation/analysis under headings:\n"
" **High Stress/Anxiety:** ... (if relevant)\n"
" **Moderate Fitness & Mobility:** ... (if relevant)\n"
" **Gut Health:** ... (if relevant)\n"
" **No More Insomnia:** ... (if relevant)\n"
" **Focus Flow:** ...\n"
" **Justification for Exclusion:** ...\n"
" etc.\n\n"
"Make sure your headings are prefixed with double asterisks, e.g. `**High Stress/Anxiety:**`.\n\n"
"Return everything as a single string. The important part:\n"
" - The JSON array is fully valid JSON.\n"
" - After that, the text analysis uses headings.\n\n"
)
)
recommend_chain = LLMChain(llm=chat_model, prompt=improved_recommend_prompt_template)
def generate_recommendations(problems: Dict[str, float]) -> str:
"""
Runs the LLMChain with the updated prompt.
Returns a string containing:
1) A JSON array on its own line
2) Explanations for each recommended or excluded package.
"""
recommendations = recommend_chain.run(problems=json.dumps(problems))
return recommendations.strip()
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