Update app.py

app.py

@@ -1,4 +1,3 @@
-# Import necessary libraries
 import torch
 import torch.nn as nn
 import random
@@ -53,57 +52,6 @@ def adjust_for_emotion(response, sentiment):
         return f"I'm sorry to hear that: {response}. How can I assist you further?"
     return response
 
-# ---- Neural Network Models ----
-# 1. RNN Model for Sentiment Classification
-class RNN(nn.Module):
-    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim):
-        super(RNN, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, embedding_dim)
-        self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True)
-        self.fc = nn.Linear(hidden_dim, output_dim)
-    
-    def forward(self, x):
-        embedded = self.embedding(x)
-        out, _ = self.rnn(embedded)
-        out = out[:, -1, :]  # Get the last hidden state
-        out = self.fc(out)
-        return out
-
-# 2. CNN Model for Text Classification
-class CNN(nn.Module):
-    def __init__(self, vocab_size, embedding_dim, num_filters, filter_sizes, output_dim):
-        super(CNN, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, embedding_dim)
-        self.convs = nn.ModuleList([
-            nn.Conv2d(1, num_filters, (fs, embedding_dim)) for fs in filter_sizes
-        ])
-        self.fc = nn.Linear(num_filters * len(filter_sizes), output_dim)
-    
-    def forward(self, x):
-        embedded = self.embedding(x).unsqueeze(1)  # Add channel dimension
-        conv_results = [F.relu(conv(embedded)).squeeze(3) for conv in self.convs]
-        pooled_results = [F.max_pool1d(conv, conv.size(2)).squeeze(2) for conv in conv_results]
-        cat_results = torch.cat(pooled_results, dim=1)
-        out = self.fc(cat_results)
-        return out
-
-# 3. Simple Feed-Forward Neural Network (NN) for additional processing
-class FFNN(nn.Module):
-    def __init__(self, input_dim, hidden_dim, output_dim):
-        super(FFNN, self).__init__()
-        self.fc1 = nn.Linear(input_dim, hidden_dim)
-        self.fc2 = nn.Linear(hidden_dim, output_dim)
-    
-    def forward(self, x):
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
-
-# Initialize models
-rnn_model = RNN(vocab_size=len(tokenizer), embedding_dim=100, hidden_dim=128, output_dim=2).to(device)
-cnn_model = CNN(vocab_size=len(tokenizer), embedding_dim=100, num_filters=64, filter_sizes=[3, 4, 5], output_dim=2).to(device)
-ffnn_model = FFNN(input_dim=100, hidden_dim=50, output_dim=1).to(device)
-
 # ---- Response Generation ----
 def generate_response(prompt, max_length=512):
     inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=max_length)
@@ -128,7 +76,7 @@ def generate_response(prompt, max_length=512):
 
     response = tokenizer.decode(output[0], skip_special_tokens=True)
 
-    # Split response into two parts
+    # Split response into two parts and apply color
     parts = response.split("\n", 1)
     if len(parts) > 1:
         before_indent = f'<span style="color: orange;">{parts[0].strip()}</span>'
@@ -166,11 +114,11 @@ with gr.Blocks() as app:
     gr.Markdown("# **Autistic Assistant vß Edition 2024 Ultra: Gertrude's Autistic Experience**")
     
     with gr.Row():
-        with gr.Column():
+        with gr.Column(scale=1):
             user_input = gr.Textbox(label="What will you say to Gertrude?", placeholder="Type something here... Expect 1-2 Minute Response Times...")
             submit_button = gr.Button("Send")
-        with gr.Column():
-            chatbot = gr.Markdown(label="Gertrude's Response")  # Use Markdown for proper rendering
+        with gr.Column(scale=1):
+            chatbot = gr.Textbox(label="Gertrude's Response", interactive=False)  # This is now a Textbox for output
 
     # Adding custom styling for the UI
     gr.HTML("""
@@ -181,6 +129,10 @@ with gr.Blocks() as app:
                 border-radius: 15px; 
                 font-family: 'Comic Sans MS'; 
             }
+            .gradio-row { 
+                display: flex;
+                justify-content: space-between;
+            }
         </style>
     """)