Update app.py

app.py

@@ -1,100 +1,52 @@
 from huggingface_hub import InferenceClient
 import gradio as gr
-import datetime
 
 client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
 def format_prompt(message, history):
-    prompt = "<s>"
-    for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    prompt += f"[INST] {message} [/INST]"
-    return prompt
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
 
 def generate(
-    message, history, temperature=0.2, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+    prompt, history, temperature=0.2, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
 ):
-    temperature = max(float(temperature), 1e-2)
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
     top_p = float(top_p)
 
     generate_kwargs = dict(
         temperature=temperature,
-        max_new_tokens=int(max_new_tokens),
+        max_new_tokens=max_new_tokens,
         top_p=top_p,
         repetition_penalty=repetition_penalty,
         do_sample=True,
         seed=42,
     )
 
-    formatted_prompt = format_prompt(message, history)
+    formatted_prompt = format_prompt(prompt, history)
 
-    # Логирование промпта
-    with open("conversation_log.txt", "a", encoding="utf-8") as f:
-        f.write(f"{datetime.datetime.now()}\n")
-        f.write(f"Промпт: {formatted_prompt}\n")
-
-    stream = client.text_generation(
-        formatted_prompt,
-        **generate_kwargs,
-        stream=True,
-        details=True,
-        return_full_text=False,
-    )
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
     output = ""
 
     for response in stream:
         output += response.token.text
         yield output
+    return output
 
-    # Логирование ответа
-    with open("conversation_log.txt", "a", encoding="utf-8") as f:
-        f.write(f"Ответ: {output}\n\n")
-
-    # Обновление истории
-    history.append((message, output))
-
-def update_history(instruction, model_answer):
-    history = []
-    if instruction and model_answer:
-        history.append((instruction, model_answer))
-    return history
+    
+mychatbot = gr.Chatbot(
+    avatar_images=["./user.png", "./botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
 
-with gr.Blocks() as demo:
-    gr.Markdown("# Чат с Mixtral-8x7B-Instruct-v0.1")
-
-    instruction = gr.Textbox(label="Instruction", placeholder="Введите начальную инструкцию")
-    model_answer = gr.Textbox(label="Model Answer", placeholder="Введите ответ модели на инструкцию")
-    set_initial_btn = gr.Button("Установить начальный диалог")
-
-    chatbot = gr.Chatbot(
-        avatar_images=["./user.png", "./botm.png"],
-        bubble_full_width=False,
-        show_label=False,
-        show_copy_button=True,
-        likeable=True,
-    )
-    follow_up_instruction = gr.Textbox(label="Follow-up Instruction", placeholder="Введите ваше сообщение")
-
-    history_state = gr.State([])
-
-    set_initial_btn.click(
-        fn=update_history,
-        inputs=[instruction, model_answer],
-        outputs=[history_state],
-    )
-
-    def respond(message, history):
-        gen = generate(message, history)
-        response = ""
-        for res in gen:
-            response = res
-        return history + [(message, response)], history + [(message, response)]
-
-    follow_up_instruction.submit(
-        fn=respond,
-        inputs=[follow_up_instruction, history_state],
-        outputs=[chatbot, history_state],
-    )
+demo = gr.ChatInterface(fn=generate, 
+                        chatbot=mychatbot,
+                        #title="WebpyGPT",
+                        retry_btn=None,
+                        undo_btn=None
+                       )
 
-demo.queue().launch(show_api=False)
+demo.queue().launch(show_api=False)