import streamlit as st
from huggingface_hub import InferenceClient

client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")

class GroupTherapyAgent:
    def __init__(self, client):
        self.client = client
        self.max_length = 64
        
    def get_response(self, user_question):
        # Make an API call to the model and get the response
        response = self.client(inputs=user_question, parameters={"max_length": self.max_length, "num_beams": 4}, options={"wait_for_model": True})
        return response['generated_text']


class GroupTherapyApplication:
    def __init__(self, client):
        self.agents = [GroupTherapyAgent(client) 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(client)
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}")

# 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:
        # Call to the GroupTherapyApplication to get real responses
        responses = app.get_advice(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.")