Update app.py

app.py

@@ -2,8 +2,6 @@ import torch
 import torch.nn as nn
 import random
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
-from textblob import TextBlob
-import gradio as gr
 import pickle
 import numpy as np
 import torch.nn.functional as F
@@ -40,18 +38,6 @@ def load_memory(filename='chat_memory.pkl'):
 
 session_memory = load_memory()
 
-# ---- Sentiment Analysis ----
-def analyze_sentiment(text):
-    blob = TextBlob(text)
-    return blob.sentiment.polarity  # Range from -1 (negative) to 1 (positive)
-
-def adjust_for_emotion(response, sentiment):
-    if sentiment > 0.2:
-        return f"That's wonderful! I'm glad you're feeling good: {response}"
-    elif sentiment < -0.2:
-        return f"I'm sorry to hear that: {response}. How can I assist you further?"
-    return response
-
 # ---- Response Generation ----
 def generate_response(prompt, max_length=512):
     inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=max_length)
@@ -76,33 +62,27 @@ def generate_response(prompt, max_length=512):
 
     response = tokenizer.decode(output[0], skip_special_tokens=True)
 
-    # Split response into two parts and apply color
+    # Split response into two parts, where the second indent is considered the "inner thoughts"
     parts = response.split("\n", 1)
     if len(parts) > 1:
-        before_indent = f'<span style="color: orange;">{parts[0].strip()}</span>'
-        after_indent = f'<span style="color: blue;">Inner Thoughts: {parts[1].strip()}</span>'
-        colored_response = before_indent + '\n' + after_indent
+        before_indent = parts[0].strip()
+        after_indent = "Inner Thoughts: " + parts[1].strip()
+        final_response = before_indent + '\n' + after_indent
     else:
-        colored_response = f'<span style="color: orange;">{response.strip()}</span>'
+        final_response = response.strip()
 
-    return colored_response
+    return final_response
 
 # ---- Interactive Chat Function ----
 def advanced_agi_chat(user_input):
     session_memory.append({"input": user_input})
     save_memory(session_memory)
 
-    # Sentiment analysis of user input
-    user_sentiment = analyze_sentiment(user_input)
-
     # Generate the response based on the prompt
     prompt = f"User: {user_input}\nAutistic-Gertrude:"
     response = generate_response(prompt)
 
-    # Adjust the response based on sentiment
-    adjusted_response = adjust_for_emotion(response, user_sentiment)
-
-    return adjusted_response
+    return response
 
 # ---- Gradio Interface ----
 def chat_interface(user_input):
@@ -110,6 +90,8 @@ def chat_interface(user_input):
     return response
 
 # ---- Gradio App Setup ----
+import gradio as gr
+
 with gr.Blocks() as app:
     gr.Markdown("# **Autistic Assistant vß Edition 2024 Ultra: Gertrude's Autistic Experience**")