Update app.py

app.py

@@ -1,30 +1,20 @@
-
 import streamlit as st
 from huggingface_hub import InferenceClient
 
-client = InferenceClient(
-    "mistralai/Mistral-7B-Instruct-v0.1"
-)
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")
 
 class GroupTherapyAgent:
-    def __init__(self, client, tokenizer):
+    def __init__(self, client):
         self.client = client
-        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.client.generate(input_ids, max_length=self.max_length, num_beams=4)
-        return output
+        response = self.client(text=user_question, max_length=self.max_length, num_beams=4)
+        return response[0]['generated_text']
 
 class GroupTherapyApplication:
-    def __init__(self, client, tokenizer):
-        self.agents = [GroupTherapyAgent(client, tokenizer) for _ in range(4)]
+    def __init__(self, client):
+        self.agents = [GroupTherapyAgent(client) for _ in range(4)]
 
     def get_advice(self, user_question):
         advice = []
@@ -33,23 +23,10 @@ class GroupTherapyApplication:
             advice.append(response)
         return advice
 
-app = GroupTherapyApplication(client, tokenizer)
+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}")
 
-# 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")
 
@@ -59,8 +36,8 @@ user_question = st.text_area("Enter your question or share your experience:", he
 # 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) 
+        # 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}")
@@ -69,4 +46,4 @@ if st.button("Get Advice"):
 
 # Footer
 st.markdown("---")
-st.caption("Disclaimer: The responses are simulated and for demonstration purposes only.")
+st.caption("Disclaimer: The responses are simulated and for demonstration purposes only.")