Update app.py

app.py

@@ -1,22 +1,42 @@
-@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")
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import spaces
+from threading import Thread
+
+# 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)
+
+MAX_INPUT_TOKEN_LENGTH = 4096  # You may need to adjust this value
+
+@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})
+ 
+    input_ids = tokenizer.apply_chat_template(messages, 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.")
@@ -24,20 +44,40 @@ def generate(
 
     streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
     generate_kwargs = dict(
-        {"input_ids": input_ids},
+        input_ids=input_ids,
         streamer=streamer,
-        max_new_tokens=max_new_tokens,
+        max_new_tokens=max_tokens,
         do_sample=True,
         top_p=top_p,
-        top_k=top_k,
         temperature=temperature,
-        num_beams=1,
         repetition_penalty=repetition_penalty,
     )
-    t = Thread(target=model.generate, kwargs=generate_kwargs)
-    t.start()
+    
+    thread = Thread(target=model.generate, kwargs=generate_kwargs)
+    thread.start()
+
+    def stream():
+        for text in streamer:
+            yield text
+
+    return stream()
+
+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="Repetition Penalty"),
+    ],
+)
 
-    outputs = []
-    for text in streamer:
-        outputs.append(text)
-        yield "".join(outputs)
+if __name__ == "__main__":
+    demo.launch(share=True)