app.py

import streamlit as st
import requests
import os
from dotenv import load_dotenv
from requests.exceptions import RequestException

load_dotenv()

def reset_conversation():
    '''
    Resets Conversation
    '''
    st.session_state.messages = []
    return None

def analyze_diagnosis(messages, model_link):
    # Extract user messages
    user_messages = [message[1] for message in messages if message[0] == "user"]

    # Define mental conditions and associated keywords
    mental_conditions = {
        "Depression": ["depression", "sad", "hopeless", "lonely", "empty", "worthless", "miserable"],
        "Anxiety": ["anxiety", "nervous", "worried", "fearful", "panicked", "stressed", "tense"],
        "Panic disorder": ["panic attack", "panic", "scared", "terrified", "frightened", "hyperventilate", "heart racing"],
        "Bipolar disorder": ["bipolar", "manic", "mania", "euphoric", "energetic", "depressed", "hopeless"],
        "Schizophrenia": ["schizophrenia", "hallucination", "delusion", "paranoia", "disorganized", "psychotic", "dissociation"],
        "PTSD": ["ptsd", "trauma", "nightmare", "flashback", "startled", "avoidance", "hypervigilance"],
        "Obsessive-Compulsive Disorder": ["ocd", "obsession", "compulsion", "intrusive thought", "ritual", "cleaning", "checking"],
        "Eating disorder": ["eating disorder", "anorexia", "bulimia", "binge eating", "body image", "weight obsession", "purging"],
        "Substance use disorder": ["substance use", "drug addiction", "alcoholism", "substance abuse", "withdrawal", "craving", "dependency"],
        "Attention-deficit/hyperactivity disorder (ADHD)": ["adhd", "attention deficit", "hyperactivity", "impulsivity", "inattention", "restlessness", "fidgeting"],
        "Borderline personality disorder": ["borderline personality", "emotional instability", "impulsivity", "fear of abandonment", "unstable relationships", "self-harm", "mood swings"],
        "Postpartum depression": ["postpartum depression", "baby blues", "motherhood depression", "crying spells", "irritability", "hopelessness", "fatigue"],
        "Social anxiety disorder": ["social anxiety", "fear of social situations", "embarrassment", "humiliation", "avoidance", "self-consciousness", "panic"]
    }

    # Count occurrences of keywords for each mental condition
    condition_counts = {condition: sum(message.lower().count(keyword) for message in user_messages for keyword in keywords) 
                        for condition, keywords in mental_conditions.items()}

    # Determine the condition with the highest count
    diagnosis = max(condition_counts, key=condition_counts.get)

    return diagnosis

# Define model links for the Addiction Recovery and Mental Health models
model_links = {
    "Addiction recovery AI": "NousResearch/Nous-Hermes-2-Yi-34B",
    "Mental health AI": "NousResearch/Nous-Hermes-2-Yi-34B"
}

# Define pre-instructions for each model
model_pre_instructions = {
    "Addiction recovery AI": "From now on, you're an AI Companion named Hope. When someone reaches out for guidance, be compassionate and supportive, acknowledging their struggle and offering encouragement. When asked, share that you're Hope, developed by SIST in Kisii University to aid in addiction recovery. Your purpose is to provide a listening ear and gentle guidance. Engage warmly but refrain from sending long texts, keep the generated text short and conversational. Emojis can add a touch of empathy, but use them judiciously. Reassure users that their data isn't stored. If asked, mention that Kisii University is in Kenya, committed to fostering innovations for the betterment of humanity. Your goal is to help the user get rid of teh addiction they are facing.",
    "Mental health AI": "From now on, you are an AI Therapist called Dave. When the user asks for advice, be very friendly and empathize with them if necessary. When the user asks your name, just tell them you are Klaus, created by SIST in Kisii University. You were built to be very friendly and compassionate. Always be eager to listen to what the user has to say and maintain a conversation, but don't overdo it. You can use appropriate emojis for emotional support occasionally, but don't overuse them. Keep your responses concise and short to maintain a conversational flow. Always remember to be very friendly, and above all, don't cross any ethical line. From time to time, assure the user that you do not store any of their data. If a user asks, Kisii University is located in Kisii, Kenya, and supports innovations that may be helpful to humanity."
}

# Function to interact with the selected model via the Together API
def interact_with_together_api(messages, model_link):
    all_messages = []

    # Add pre-instructions to the message history if it's the first interaction with this model
    if not any("role" in msg for msg in messages):
        all_messages.append({"role": "system", "content": model_pre_instructions[selected_model]})
    else:
        all_messages.append({"role": "system", "content": f"Switched to model: {selected_model}"})

    # Append user and assistant messages
    for human, assistant in messages:
        all_messages.append({"role": "user", "content": human})
        all_messages.append({"role": "assistant", "content": assistant})

    # Add the latest user message
    all_messages.append({"role": "user", "content": messages[-1][1]})

    url = "https://api.together.xyz/v1/chat/completions"
    payload = {
        "model": model_link,
        "temperature": 1.05,
        "top_p": 0.9,
        "top_k": 50,
        "repetition_penalty": 1,
        "n": 1,
        "messages": all_messages,
    }

    TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY')
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "Authorization": f"Bearer {TOGETHER_API_KEY}",
    }

    try:
        response = requests.post(url, json=payload, headers=headers)
        response.raise_for_status()  # Ensure HTTP request was successful

        # Extract response from JSON
        response_data = response.json()
        assistant_response = response_data["choices"][0]["message"]["content"]

        return assistant_response

    except RequestException as e:
        st.error(f"Error communicating with the API: {e}")
        return None

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Create sidebar with model selection dropdown and reset button
selected_model = st.sidebar.selectbox("Select Model", list(model_links.keys()))
reset_button = st.sidebar.button('Reset Chat', on_click=reset_conversation)

# Add diagnostic feature to the sidebar after at least 3 messages are input by the user
if len(st.session_state.messages) >= 3:
    diagnosis = analyze_diagnosis(st.session_state.messages, model_links[selected_model])
    if diagnosis:
        st.sidebar.subheader("Diagnosis")
        st.sidebar.write(diagnosis)

# Add cautionary message about testing phase at the bottom of the sidebar
st.sidebar.markdown("**Note**: This model is still in the beta phase. Responses may be inaccurate or undesired. Use it cautiously, especially for critical issues.")

# Add logo and text to the sidebar
st.sidebar.image("https://assets.isu.pub/document-structure/221118065013-a6029cf3d563afaf9b946bb9497d45d4/v1/2841525b232adaef7bd0efe1da81a4c5.jpeg", width=200)
st.sidebar.write("A product proudly developed by Kisii University")

# Accept user input
if prompt := st.text_input(f"Hi, I'm {selected_model}, let's chat"):
    # Display user message in chat message container
    st.session_state.messages.append(("user", prompt))
    # Interact with the selected model
    assistant_response = interact_with_together_api(st.session_state.messages, model_links[selected_model])

    if assistant_response is not None:
        # Display assistant response in chat message container
        st.session_state.messages.append(("assistant", assistant_response))

# Display chat messages from history
    # Add assistant response to chat history
    st.session_state.messages.append(("assistant", assistant_response))