import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

class GroupTherapyAgent:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer
        self.max_length = 64

    def get_response(self, user_question):
        input_ids = self.tokenizer.encode(user_question, return_tensors="pt").squeeze()
        response_ids = self.generate_response(input_ids)
        response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
        return response

    def generate_response(self, input_ids):
        output = self.model.generate(input_ids, max_length=self.max_length, num_beams=4)
        return output

class GroupTherapyApplication:
    def __init__(self, model, tokenizer):
        self.agents = [GroupTherapyAgent(model, tokenizer) for _ in range(4)]

    def get_advice(self, user_question):
        advice = []
        for agent in self.agents:
            response = agent.get_response(user_question)
            advice.append(response)
        return advice

app = GroupTherapyApplication(model, tokenizer)
advice = app.get_advice("I feel anxious when I have to speak in front of a group of people.")
print(f"Advice from Agents:\n{advice}")

# Assuming the backend functionality is defined in a separate file, say 'therapy_app.py'
# from therapy_app import GroupTherapyApplication

# Temporary function to simulate responses (replace with real model interactions later)
def get_simulated_responses(question):
    # These are just placeholder responses. Replace this with calls to your model.
    return [
        f"Agent 1 says: Regarding your concern, '{question}', I think...",
        f"Agent 2 says: In response to '{question}', my advice would be...",
        f"Agent 3 says: I understand that '{question}' can be challenging. My suggestion...",
        f"Agent 4 says: From my experience, '{question}' is often addressed by..."
    ]

# Streamlit App Layout
st.title("Group Therapy Session App")

# User question input
user_question = st.text_area("Enter your question or share your experience:", height=150)

# Button to submit question
if st.button("Get Advice"):
    if user_question:
        # Replace the following line with a call to your actual model
        responses = get_simulated_responses(user_question) 

        for idx, response in enumerate(responses, start=1):
            st.markdown(f"**Agent {idx}:** {response}")
    else:
        st.warning("Please enter a question or experience to share.")

# Footer
st.markdown("---")
st.caption("Disclaimer: The responses are simulated and for demonstration purposes only.")