Create app.py

app.py

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+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+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.")