Update app.py

app.py

@@ -1,64 +1,84 @@
+from threading import Thread
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("agentica-org/DeepScaleR-1.5B-Preview")
-
-
-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})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are 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=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+import spaces
+tokenizer = AutoTokenizer.from_pretrained("agentica-org/DeepScaleR-1.5B-Preview")
+model = AutoModelForCausalLM.from_pretrained("agentica-org/DeepScaleR-1.5B-Preview", device_map='auto')
 
 
-if __name__ == "__main__":
+def preprocess_messages(history):
+    messages = []
+
+    for idx, (user_msg, model_msg) in enumerate(history):
+        if idx == len(history) - 1 and not messages:
+            messages.append({"role": "user", "content": user_msg})
+            break
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if model_msg:
+            messages.append({"role": "assistant", "content": messages})
+
+    return messages
+
+
+@spaces.GPU()
+def predict(history, max_length, top_p, temperature):
+    messages = preprocess_messages(history)
+    model_inputs = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True
+    ).to(model.device)
+    streamer = TextIteratorStreamer(tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = {
+        "input_ids": model_inputs["input_ids"],
+        "attention_mask": model_inputs["attention_mask"],
+        "streamer": streamer,
+        "max_new_tokens": max_length,
+        "do_sample": True,
+        "top_p": top_p,
+        "temperature": temperature,
+        "repetition_penalty": 1.2,
+    }
+
+    generate_kwargs['eos_token_id'] = tokenizer.encode("<|user|>")
+
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    for new_token in streamer:
+        if new_token:
+            history[-1][1] += new_token
+        yield history
+
+
+def main():
+    with gr.Blocks() as demo:
+        gr.HTML("""<h1 align="center">GLM-Edge-Chat Gradio Demo</h1>""")
+
+        with gr.Row():
+            with gr.Column(scale=3):
+                chatbot = gr.Chatbot()
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                user_input = gr.Textbox(show_label=True, placeholder="Input...", label="User Input")
+                submitBtn = gr.Button("Submit")
+                emptyBtn = gr.Button("Clear History")
+            with gr.Column(scale=1):
+                max_length = gr.Slider(0, 8192, value=4096, step=1.0, label="Maximum length", interactive=True)
+                top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True)
+                temperature = gr.Slider(0.01, 1, value=0.6, step=0.01, label="Temperature", interactive=True)
+
+        # Define functions for button actions
+        def user(query, history):
+            return "", history + [[query, ""]]
+
+        submitBtn.click(user, [user_input, chatbot], [user_input, chatbot], queue=False).then(
+            predict, [chatbot, max_length, top_p, temperature], chatbot
+        )
+        emptyBtn.click(lambda: (None, None), None, [chatbot], queue=False)
+
+    demo.queue()
     demo.launch()
+
+
+if __name__ == "__main__":
+    main()