Update app.py

app.py

@@ -1,74 +1,43 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import spaces
-
-# Load model and tokenizer
-model_name = "Magpie-Align/MagpieLM-4B-Chat-v0.1"
-
-device = "cuda" # the device to load the model onto
-tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto"
-)
-model.to(device)
-
-@spaces.GPU(enable_queue=True)
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens=2048,
-    temperature=0.6,
-    top_p=0.9,
-    repetition_penalty=1.0,
-):
-    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})
- 
-    text = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
-    model_inputs = tokenizer([text], return_tensors="pt").to(device)
-
-    streamer = gr.utils.StreamingTextStreamer(tokenizer, skip_special_tokens=True, skip_prompt=True)
-
-    _ = model.generate(
-        model_inputs.input_ids,
-        max_new_tokens = max_tokens,
-        temperature = temperature,
-        top_p = top_p,
+@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    system_prompt: str,
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    conversation = []
+    if system_prompt:
+        conversation.append({"role": "system", "content": system_prompt})
+    for user, assistant in chat_history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {"input_ids": input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
         repetition_penalty=repetition_penalty,
-        streamer=streamer
     )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
 
-    return streamer
-
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are Magpie, a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.6, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.9,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-        gr.Slider(minimum=0.5, maximum=1.5, value=1.0, step=0.1, label="Repetation Penalty"),
-    ],
-)
-
-if __name__ == "__main__":
-    demo.launch(share=True)
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)