Spaces:
Sleeping
Sleeping
File size: 2,802 Bytes
03e5d11 633e9a7 70f52b9 633e9a7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
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.") |