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import json |
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from typing import Dict |
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from langchain import PromptTemplate, LLMChain |
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from models import chat_model |
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improved_recommend_prompt_template = PromptTemplate( |
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input_variables=["problems"], |
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template=( |
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"You are a wellness recommendation assistant. Given the following problem severity percentages:\n" |
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"{problems}\n\n" |
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"Based on these percentages and the available wellness packages:\n" |
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"1. Fitness & Mobility | Tagline: 'Enhance Mobility. Boost Fitness.'\n" |
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"2. No More Insomnia | Deep Rest | Tagline: 'Reclaim Your Sleep. Restore Your Mind.'\n" |
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"3. Focus Flow | Clarity Boost | Tagline: 'Stay Focused. Stay Productive.'\n" |
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"4. Boost Energy | Tagline: 'Fuel Your Day. Boost Your Energy.'\n" |
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"5. Chronic Care | Chronic Support | Tagline: 'Ongoing Support for Chronic Wellness.'\n" |
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"6. Mental Wellness | Calm Mind | Tagline: 'Find Peace of Mind, Every Day.'\n\n" |
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"Carefully analyze these percentages and consider nuanced differences between the areas. " |
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"Your goal is to recommend the most appropriate wellness packages based on a detailed assessment of these numbers, " |
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"not just fixed thresholds. Consider the following guidelines:\n\n" |
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"- If one area is extremely high (above 70) while others are lower, prioritize a package targeting that area.\n" |
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"- If multiple areas are high or near high (e.g., above 60), consider recommending multiple specialized packages or a comprehensive program.\n" |
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"- If all areas are moderate (between 30 and 70), recommend a balanced wellness package that addresses overall health.\n" |
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"- If all areas are low, a general wellness package might be sufficient.\n" |
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"- Consider borderline cases and recommend packages that address both current issues and preventive measures.\n\n" |
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"Return the recommended wellness packages in a JSON array format. " |
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"Each item should be exactly one of the following package names: " |
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"\"Fitness & Mobility\", \"No More Insomnia\", \"Focus Flow\", \"Boost Energy\", \"Chronic Care\", \"Mental Wellness\"." |
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) |
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) |
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recommend_chain = LLMChain(llm=chat_model, prompt=improved_recommend_prompt_template) |
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def generate_recommendations(problems: Dict[str, float]) -> str: |
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recommendations = recommend_chain.run(problems=json.dumps(problems)) |
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return recommendations.strip() |
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