app.py

import os
import gradio as gr
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
import openai

OPENAI_API_KEY = os.environ['OPENAI_API_KEY']

emotional_states_list = [
    "angry",
    "bored",
    "confident",
    "dazed",
    "embarrased",
    "energized",
    "fine",
    "flirty",
    "focused",
    "happy",
    "playful",
    "uncomfortable",
    "sad",
    "scared",
    "asleep"
]

intensity_levels = ["none", "low", "medium", "high"]

def self_care_activity(*emotional_states):
    activities = {
        "angry": {
            "low": "Try meditating for 5 minutes.",
            "medium": "Go for a walk to cool off.",
            "high": "Do an intense workout session."
        },
        "bored": {
            "low": "Listen to a podcast or audiobook.",
            "medium": "Try a new hobby or craft.",
            "high": "Plan a day trip or outing."
        },
        # Add the remaining emotional states and suggestions here
    }
    
    #print(emotional_states)
    
    emotional_dict ={}

    for idx, intensity in enumerate(emotional_states):
        if intensity != 'none':
            emotional_dict[emotional_states_list[idx]] = emotional_states[idx]
            
            #return activities.get(state, {}).get(intensity, "No activity found for this combination.")
    print(emotional_dict)    
    
    
    prompt_input = '''

    You are a motivational coach, a kind and caring psychologist and an expert in helping unleashing a person's full potential in anything.
    
    You are given a set of emotional state of an individual below:
    
    {}
    
    If the emotional state is selected as "none", ignore that emotional state.
    
    From the table above, take into account the individual's emotional state.
    
    You want to make the individual feel safe and cared-for.
    
    Think step by step and directly recommend 3 self-care activities in bullet points for the individual based on the individual's emotional state.

    '''.format(emotional_dict)

    template = '''
    {history}
    {human_input}
    '''
    prompt = PromptTemplate(
        input_variables=["history", "human_input"], 
        template=template
    )

    chatgpt_chain = LLMChain(
        llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0.6,openai_api_key=OPENAI_API_KEY), 
        prompt=prompt, 
        verbose=True, 
        memory=ConversationBufferWindowMemory(k=10),
    )
    
    output = chatgpt_chain.predict(human_input=prompt_input)
    return output

inputs = []
for state in emotional_states_list:
    inputs.append(gr.inputs.Radio(intensity_levels, label=state.capitalize(), default="none"))

iface = gr.Interface(
    fn=self_care_activity,
    inputs=inputs,
    outputs=gr.outputs.Textbox(label="Suggested Self-Care Activity"),
    title="Self-Care Activity Suggestion",
    description="Select an emotional state and intensity level to receive a self-care activity suggestion. \n Please note that this is NOT a professional mental health tool and should not be used as such.  \n If you are having any severe symptoms or self-harm thoughts, please consult a professional IMMEDIATELY."
)

iface.launch()