tranformation of history added

app.py

@@ -10,8 +10,17 @@ model = AutoModelForCausalLM.from_pretrained("IlyaGusev/saiga_llama3_8b", torch_
 model = model
 
 
+def transform_history(history):
+    transformed_history = []
+    for qa_pair in history:
+        transformed_history.append({"role": "user", "content": qa_pair[0]})
+        transformed_history.append({"role": "assistant", "content": qa_pair[1]})
+    return transformed_history
+
+
 def predict(message, history):
     # print(history) [[вопрос1, ответ1], [вопрос2, ответ2]...]
+    history = transform_history(history)
     history_transformer_format = history + [{"role": "user", "content": message}, 
                                             {"role": "assistant", "content": ""}]
 
@@ -32,8 +41,8 @@ def predict(message, history):
 
     partial_message = ""
     for new_token in streamer:
-        print(new_token)
-        if new_token != '<':
+        # print(new_token)
+        if new_token != 'assistant':
             partial_message += new_token
             yield partial_message