Update app.py

app.py

@@ -1,52 +1,49 @@
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
- 
+
+# Model name
 MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
- 
+
+# Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
- 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
- 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
- 
-    messages.append({"role": "user", "content": message})
- 
-    inputs = tokenizer(message, return_tensors="pt").to("cpu")
+
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}]
+    
+    # Add chat history to messages
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
+    
+    messages.append({"role": "user", "content": message})
+    
+    # Tokenize input
+    inputs = tokenizer(message, return_tensors="pt").to("cpu")
+    
+    # Generate response
     with torch.no_grad():
-        outputs = model.generate(**inputs, max_length=max_tokens, temperature=temperature, top_p=top_p)
+        outputs = model.generate(
+            **inputs, max_length=max_tokens, temperature=temperature, top_p=top_p
+        )
+    
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
     return response
- 
+
+# Define Gradio interface
 demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=512, value=64, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=1.5, value=0.3, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=0.8,
-            value=0.75,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=512, value=64, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=1.5, value=0.3, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=0.8, value=0.75, step=0.05, label="Top-p (nucleus sampling)"),
+    ],
 )
- 
+
+# Launch Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()