Update app.py

app.py

@@ -59,15 +59,15 @@ def get_model(base_model: str = "bigcode/starcoder",):
     return tokenizer, model
 
 
-class RNN_model(nn.Module):
+class GRU_model(nn.Module):
   def __init__(self):
     super().__init__()
 
-    self.rnn= nn.GRU(input_size=1080, hidden_size=240,num_layers=1, nonlinearity= 'relu', bias= True)
+    self.gru= nn.GRU(input_size=1080, hidden_size=240,num_layers=1, nonlinearity= 'relu', bias= True)
     self.output= nn.Linear(in_features=240, out_features=24)
 
   def forward(self, x):
-    y, hidden= self.rnn(x)
+    y, hidden= self.gru(x)
     #print(y.shape)
     #print(hidden.shape)
     x= self.output(y)
@@ -112,7 +112,7 @@ vectorizer= nltk_u.vectorizer()
 vectorizer.fit(train_data.text)
 
 # Model and transforms preparation
-model= RNN_model()
+model= GRU_model()
 # Load state dict
 model.load_state_dict(torch.load(
     f= 'pretrained_symtom_to_disease_model.pth',
@@ -183,7 +183,7 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
     time.sleep(2)
     return "", chat_history
 '''
-def respond(message, chat_history, base_model = "microsoft/phi-2", device=device): # "meta-llama/Meta-Llama-3-70B"
+def respond(message, chat_history, base_model = "self_GRU", device=device): # "meta-llama/Meta-Llama-3-70B"
     if base_model != "microsoft/phi-2":
             # Random greetings in list format
         greetings = [