test

README.md

@@ -4,7 +4,7 @@ emoji: 🐨
 colorFrom: green
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.5.0
+sdk_version: 5.6.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,84 @@
+from threading import Thread
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+import space
+tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-edge-1.5b-chat")
+model = AutoModelForCausalLM.from_pretrained("THUDM/glm-edge-1.5b-chat", device='auto')
+
+
+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
+    )
+    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 Chat 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()

requirements.txt

@@ -0,0 +1,4 @@
+git+https://github.com/huggingface/transformers.git
+gradio==5.6.0
+space
+