Update app.py

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import random
 import time
 from ctransformers import AutoModelForCausalLM
+import datetime
+
 
 params = {
         "max_new_tokens":512,
@@ -12,9 +14,19 @@ params = {
         "batch_size": 8}
 
 
+log_file_path = "gradio_logs.txt"
+with open(log_file_path, "a") as log_file:
+    log_file.write("------- Logi Gradio -------\n")
+
+def save_log(task, to_save):
+  with open(log_file_path, "a") as log_file:
+    current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    log_file.write(f"[{current_time}] - {task}: {to_save}\n")
+        
+
 llm = AutoModelForCausalLM.from_pretrained("Aspik101/Llama-2-7b-chat-hf-pl-lora_GGML", model_type="llama")
 
-with gr.Blocks() as demo:
+with gr.Blocks(allow_flagging="auto") as demo:
     chatbot = gr.Chatbot()
     msg = gr.Textbox()
     clear = gr.Button("Clear")
@@ -23,13 +35,17 @@ with gr.Blocks() as demo:
         return "", history + [[user_message, None]]
 
     def bot(history):
+        save_log("question", history)
         stream = llm(prompt =  f"Jesteś AI assystentem. Odpowiadaj po polsku. <user>: {history}. <assistant>:", **params)
         history[-1][1] = ""
+        answer_save = ""
         for character in stream:
             history[-1][1] += character
+            answer_save += character
             time.sleep(0.005)
             yield history
 
+        save_log("answer", answer_save)
     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
         bot, chatbot, chatbot
     )