Update app.py

app.py

@@ -9,7 +9,6 @@ tf.keras.mixed_precision.set_global_policy(policy)
 strategy = tf.distribute.MultiWorkerMirroredStrategy()
 
 login(os.environ.get("hf_token"))
-
 name = "WICKED4950/GPT2mini-InstEsther0.3eV3.2"
 tokenizer = AutoTokenizer.from_pretrained(name)
 tokenizer.pad_token = tokenizer.eos_token
@@ -17,7 +16,7 @@ with strategy.scope():
     model = TFAutoModelForCausalLM.from_pretrained(name)
 
 def raw_pred(input, model, tokenizer, max_length=1024, temperature=0.2):
-    input_ids = tokenizer.encode(input.capitalize(), return_tensors='tf')
+    input_ids = tokenizer.encode(input, return_tensors='tf')
 
     # Initialize variables
     generated_ids = input_ids
@@ -61,8 +60,8 @@ def respond(message, history):
     give_mod = ""
     history = history[-3:]
     for chunk in history:
-        give_mod = give_mod + "<|SOH|>" + chunk[0] + "<|SOB|>" + chunk[1]
-    give_mod = give_mod + "<|SOH|>" + message + "<|SOB|>"
+        give_mod = give_mod + "<|SOH|>" + chunk[0].capitalize() + "<|SOB|>" + chunk[1]
+    give_mod = give_mod + "<|SOH|>" + message.capitalize() + "<|SOB|>"
     print(give_mod)
     response = ""
     for token in raw_pred(give_mod, model, tokenizer):