Update app.py

app.py

@@ -1,151 +1,87 @@
 import gradio as gr
-
 from huggingface_hub import InferenceClient
-
 from transformers import AutoTokenizer, AutoModelForCausalLM
-
 import torch
 
-# Initialize the InferenceClient
-
+# Inicjalizacja InferenceClient
 client = InferenceClient("01-ai/Yi-Coder-9B-Chat")
 
-# Initialize tokenizer and model
-
-model_path = "01-ai/Yi-Coder-9B-Chat"  # Make sure this is correct
-
+# Inicjalizacja tokenizera i modelu
+model_path = "01-ai/Yi-Coder-9B-Chat"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
-
 model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
 
 def respond(
-
     message,
-
     history: list[tuple[str, str]],
-
     system_message,
-
     max_tokens,
-
     temperature,
-
     top_p,
-
     use_local_model: bool,
-
 ):
-
+    # Przygotowanie wiadomości do kontekstu
     messages = [{"role": "system", "content": system_message}]
-
     for user, assistant in history:
-
         if user:
-
             messages.append({"role": "user", "content": user})
-
         if assistant:
-
             messages.append({"role": "assistant", "content": assistant})
-
     messages.append({"role": "user", "content": message})
 
     if use_local_model:
-
-        # Use local model
-
-        input_ids = tokenizer.encode("".join([m["content"] for m in messages]), return_tensors="pt")
-
+        # Użycie lokalnego modelu
+        input_text = "\n".join([f"{m['role']}: {m['content']}" for m in messages])
+        input_ids = tokenizer.encode(input_text, return_tensors="pt")
         input_ids = input_ids.to(model.device)
-
         
-
         with torch.no_grad():
-
             output = model.generate(
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                 input_ids,
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                 max_new_tokens=max_tokens,
-
                 temperature=temperature,
-
                 top_p=top_p,
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                 do_sample=True,
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                 pad_token_id=tokenizer.eos_token_id,
-
             )
-
         
-
         response = tokenizer.decode(output[0], skip_special_tokens=True)
-
-        yield response
-
+        yield response.split("assistant:")[-1].strip()
     else:
-
-        # Use Hugging Face Inference API
-
+        # Użycie Hugging Face Inference API
         response = ""
-
-        for message in client.text_generation(
-
-            "".join([m["content"] for m in messages]),
-
+        for chunk in client.text_generation(
+            "\n".join([f"{m['role']}: {m['content']}" for m in messages]),
             max_new_tokens=max_tokens,
-
             stream=True,
-
             temperature=temperature,
-
             top_p=top_p,
-
         ):
+            response += chunk
+            yield response.split("assistant:")[-1].strip()
 
-            response += message
-
-            yield response
-
-# Create Gradio interface
-
+# Tworzenie interfejsu Gradio
 demo = gr.ChatInterface(
-
     respond,
-
     additional_inputs=[
-
-        gr.Textbox(value="Odpowiadasz w Jezyku Polskim jesteś Coder/Developer/Programista tworzysz pełny kod..", label="System message"),
-
-        gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max new tokens"),
-
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-
+        gr.Textbox(
+            value="Odpowiadasz w języku polskim. Jesteś Coder/Developer/Programista i tworzysz pełny kod.",
+            label="Wiadomość systemowa"
+        ),
+        gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Maksymalna liczba nowych tokenów"),
+        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperatura"),
         gr.Slider(
-
             minimum=0.1,
-
             maximum=1.0,
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             value=0.95,
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             step=0.05,
-
-            label="Top-p (nucleus sampling)",
-
+            label="Top-p (próbkowanie nucleus)",
         ),
-
-        gr.Checkbox(label="Use Local Model", value=False),
-
+        gr.Checkbox(label="Użyj lokalnego modelu", value=False),
     ],
-
-    title="Advanced Chat Interface",
-
-    description="Chat with an AI model using either the Hugging Face Inference API or a local model.",
-
+    title="Zaawansowany interfejs czatu AI",
+    description="Czatuj z modelem AI, korzystając z Hugging Face Inference API lub lokalnego modelu.",
 )
 
-if name == "__main__":
-
+if __name__ == "__main__":
     demo.launch()