Update app.py

app.py

@@ -1,11 +1,12 @@
 # app.py
-# =============
-# This is a complete app.py file for a text generation app using the Qwen/Qwen2.5-Coder-0.5B-Instruct model.
-# The app uses the Gradio library to create a web interface for interacting with the model.
+# =======
+# Полная версия исправленного кода приложения для генерации текста с использованием Gradio 4.44.1
+# и модели Qwen/Qwen2.5-Coder-0.5B-Instruct.
 
 # Imports
 # =======
 import gradio as gr
+import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # Constants
@@ -19,11 +20,12 @@ def load_model_and_tokenizer():
     """
     Load the model and tokenizer from Hugging Face.
     """
+    device = "cuda" if torch.cuda.is_available() else "cpu"
     tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
     model = AutoModelForCausalLM.from_pretrained(
         MODEL_NAME,
-        torch_dtype="auto",
-        device_map="cpu"  # Ensure the model runs on the CPU
+        torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+        device_map=device
     )
     return model, tokenizer
 
@@ -37,12 +39,9 @@ def generate_response(prompt, chat_history, max_new_tokens, temperature):
     Generate a response from the model based on the user prompt and chat history.
     """
     messages = [{"role": "system", "content": SYSTEM_MESSAGE}] + chat_history + [{"role": "user", "content": prompt}]
-    text = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
-    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    # Concatenate messages into a single string for the model
+    text = "\n".join(f"{msg['role']}: {msg['content']}" for msg in messages)
+    model_inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024).to(model.device)
 
     generated_ids = model.generate(
         **model_inputs,
@@ -50,16 +49,11 @@ def generate_response(prompt, chat_history, max_new_tokens, temperature):
         do_sample=True,
         top_k=50,
         top_p=0.95,
-        temperature=temperature,
-        output_scores=True,
-        return_dict_in_generate=True,
-        return_legacy_cache=True  # Ensure legacy format is returned
+        temperature=temperature
     )
 
-    response = ""
-    for token_id in generated_ids.sequences[0][len(model_inputs.input_ids[0]):]:
-        response += tokenizer.decode([token_id], skip_special_tokens=True)
-        yield chat_history + [{"role": "assistant", "content": response}]
+    response = tokenizer.decode(generated_ids[0][model_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    return response
 
 # Clear Chat History
 # ==================
@@ -90,13 +84,12 @@ def gradio_interface():
                     temperature = gr.Slider(0.1, 1.0, value=0.7, step=0.05, label="Temperature")
 
         def respond(message, chat_history, max_new_tokens, temperature):
+            if not message.strip():
+                return chat_history, ""
             chat_history.append({"role": "user", "content": message})
-            response = ""
-            for chunk in generate_response(message, chat_history, max_new_tokens, temperature):
-                response = chunk[-1]["content"]
-                yield chat_history, ""
+            response = generate_response(message, chat_history, max_new_tokens, temperature)
             chat_history.append({"role": "assistant", "content": response})
-            yield chat_history, ""
+            return chat_history, ""
 
         submit.click(respond, [msg, chatbot, max_new_tokens, temperature], [chatbot, msg])
         msg.submit(respond, [msg, chatbot, max_new_tokens, temperature], [chatbot, msg])
@@ -111,11 +104,4 @@ if __name__ == "__main__":
 
 # Dependencies
 # =============
-# The following dependencies are required to run this app:
-# - transformers
-# - gradio
-# - torch
-# - accelerate
-#
-# You can install these dependencies using pip:
-# pip install transformers gradio torch accelerate
+# pip install transformers gradio==4.44.1 torch accelerate