Update app.py

app.py

@@ -1,142 +1,30 @@
-import os
-import time
-#import spaces
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 import gradio as gr
-from threading import Thread
-
-MODEL_LIST = ["GoidaAlignment/GOIDA-0.5B"]
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-TITLE = "<h1><center>Я СКАЗАЛ ГОООЙДА!</center></h1>"
-
-PLACEHOLDER = """
-<center>
-<p>ГООООЙДА!!</p>
-</center>
-"""
-
-# pip install transformers
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-device = "cpu" # for GPU usage or "cpu" for CPU usage
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_LIST[0])
-model = AutoModelForCausalLM.from_pretrained(MODEL_LIST[0]).to(device)
-
-
-#@spaces.GPU()
-def stream_chat(
-    message: str, 
-    history: list, 
-    temperature: float = 0.4, 
-    max_new_tokens: int = 1024, 
-    top_p: float = 1.0, 
-    top_k: int = 20, 
-    penalty: float = 1.2,
-    choice: str = "GoidaAlignment/GOIDA-0.5B"
-):
-    print(f'message: {message}')
-    print(f'history: {history}')
-
-    conversation = []
-    for prompt, answer in history:
-        conversation.extend([
-            {"role": "user", "content": prompt}, 
-            {"role": "assistant", "content": answer},
-        ])
-
-    conversation.append({"role": "user", "content": message})
-
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Загрузка токенизатора и модели
+model_name = "GoidaAlignment/GOIDA-0.5B"  # Укажите путь к вашей модели
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+    outputs = model.generate(inputs["input_ids"], max_length=200, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Интерфейс Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("# Введите запрос, и модель ответит.")
     
-
-    input_text=tokenizer.apply_chat_template(conversation,  add_generation_prompt=True, tokenize=False)
-    inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
-    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Ваш запрос", lines=4, placeholder="Введите текст")
+        with gr.Column():
+            output = gr.Textbox(label="Ответ модели", lines=6, interactive=False)
     
-    generate_kwargs = dict(
-        input_ids=inputs, 
-        max_new_tokens = max_new_tokens,
-        do_sample = False if temperature == 0 else True,
-        top_p = top_p,
-        top_k = top_k,
-        temperature = temperature,
-        streamer=streamer,
-    )
-
-    with torch.no_grad():
-        thread = Thread(target=model.generate, kwargs=generate_kwargs)
-        thread.start()
-        
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        yield buffer
-
-            
-    #print(tokenizer.decode(outputs[0]))
-
-chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
-
-with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
-    gr.HTML(TITLE)
-    gr.ChatInterface(
-        fn=stream_chat,
-        chatbot=chatbot,
-        fill_height=True,
-        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
-        additional_inputs=[
-            gr.Slider(
-                minimum=0,
-                maximum=1,
-                step=0.1,
-                value=0.4,
-                label="Temperature",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=128,
-                maximum=8192,
-                step=1,
-                value=1024,
-                label="Max new tokens",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0.0,
-                maximum=1.0,
-                step=0.1,
-                value=1.0,
-                label="top_p",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=1,
-                maximum=20,
-                step=1,
-                value=20,
-                label="top_k",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0.0,
-                maximum=2.0,
-                step=0.1,
-                value=1.2,
-                label="Repetition penalty",
-                render=False,
-            ),
-            gr.Radio(
-                ["GoidaAlignment/GOIDA-0.5B"],
-                value="494M",
-                label="Load Model",
-                render=False,
-            ),
-        ],
-        cache_examples=False,
-    )
-
+    submit_button = gr.Button("Сгенерировать")
+    submit_button.click(generate_response, inputs=prompt_input, outputs=output)
 
+# Запуск приложения
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()