app.py

import streamlit as st
import requests
import os
from dotenv import load_dotenv
from requests.exceptions import RequestException, HTTPError, ConnectionError, Timeout, TooManyRedirects, JSONDecodeError
from textblob import TextBlob

# Load environment variables
load_dotenv()

# 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 the 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 reset conversation
def reset_conversation():
    st.session_state.messages = []
    st.session_state.ask_intervention = False
    return None

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

    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}"})

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

    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()

        response_data = response.json()
        assistant_response = response_data["choices"][0]["message"]["content"]

        return assistant_response

    except (HTTPError, ConnectionError, Timeout, TooManyRedirects) as e:
        st.error(f"Error communicating with the API: {e}")
        return None

    except JSONDecodeError as e:
        st.error(f"Error decoding JSON response: {e}")
        return None

    except RequestException as e:
        st.error(f"RequestException: {e}")
        return None

# Function to perform sentiment analysis on the conversation
def analyze_sentiment(messages):
    sentiments = []
    for _, message in messages:
        blob = TextBlob(message)
        sentiment_score = blob.sentiment.polarity
        sentiments.append(sentiment_score)
    
    if sentiments:  # Check if sentiments list is not empty
        average_sentiment = sum(sentiments) / len(sentiments)
    else:
        average_sentiment = 0  # Set default sentiment to 0 if no messages are available
    
    return average_sentiment

# Initialize chat history and session state attributes
if "messages" not in st.session_state:
    st.session_state.messages = []
    st.session_state.ask_intervention = False

# Create sidebar with model selection dropdown and reset button
model_links = {
    "Addiction recovery AI": "NousResearch/Nous-Hermes-2-Yi-34B",
    "Mental health AI": "NousResearch/Nous-Hermes-2-Yi-34B"
}
selected_model = st.sidebar.selectbox("Select Model", list(model_links.keys()))
reset_button = st.sidebar.button('Reset Chat', on_click=reset_conversation)

# Accept user input with input validation
max_input_length = 100  # Maximum allowed character limit for user input
if prompt := st.text_input(f"Hi, I'm {selected_model}, let's chat (Max {max_input_length} characters)"):
    if len(prompt) > max_input_length:
        st.error(f"Maximum input length exceeded. Please limit your input to {max_input_length} characters.")
    else:
        with st.empty():
            if st.button("Send"):
                with st.spinner("Sending..."):
                    with st.chat_message("user"):
                        st.markdown(prompt)
                    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:
                        with st.empty():
                            st.markdown("AI is typing...")
                            st.empty()
                            with st.chat_message("assistant"):
                                st.markdown(assistant_response)

                            if any(keyword in prompt.lower() for keyword in ["human", "therapist", "someone", "died", "death", "help", "suicide", "suffering", "crisis", "emergency", "support", "depressed", "anxiety", "lonely", "desperate", "struggling", "counseling", "distressed", "hurt", "pain", "grief", "trauma", "abuse", "danger", "risk", "urgent", "need assistance"]):
                                if not st.session_state.ask_intervention:
                                    if st.button("After the analyzing our session you may need some extra help, so you can reach out to a certified therapist at +25493609747 Name: Ogega feel free to talk"):
                                        st.write("You can reach out to a certified therapist at +25493609747.")

                        st.session_state.messages.append(("assistant", assistant_response))

# Display conversation insights
st.sidebar.subheader("Conversation Insights")
average_sentiment = analyze_sentiment(st.session_state.messages)
st.sidebar.write(f"Average Sentiment: {average_sentiment}")

# 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")