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| import streamlit as st | |
| from huggingface_hub import InferenceClient | |
| client = InferenceClient( | |
| "mistralai/Mistral-7B-Instruct-v0.1" | |
| ) | |
| 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.") |