Create app.py

app.py

@@ -0,0 +1,133 @@
+import os
+import time
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import gradio as gr
+from threading import Thread
+
+MODEL = "THUDM/LongWriter-llama3.1-8b"
+
+TITLE = "<h1><center>LongWriter-llama3.1-8b</center></h1>"
+
+PLACEHOLDER = """
+<center>
+<p>Hi! I'm LongWriter, capable of generating 10,000+ words. How can I assist you today?</p>
+</center>
+"""
+
+CSS = """
+.duplicate-button {
+    margin: auto !important;
+    color: white !important;
+    background: black !important;
+    border-radius: 100vh !important;
+}
+h3 {
+    text-align: center;
+}
+"""
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
+model = model.eval()
+
+@spaces.GPU()
+def stream_chat(
+    message: str, 
+    history: list,
+    system_prompt: str,
+    temperature: float = 0.5, 
+    max_new_tokens: int = 32768, 
+    top_p: float = 1.0, 
+    top_k: int = 50,
+):
+    print(f'message: {message}')
+    print(f'history: {history}')
+
+    full_prompt = f"<<SYS>>\n{system_prompt}\n<</SYS>>\n\n"
+    for prompt, answer in history:
+        full_prompt += f"[INST]{prompt}[/INST]{answer}"
+    full_prompt += f"[INST]{message}[/INST]"
+
+    inputs = tokenizer(full_prompt, truncation=False, return_tensors="pt").to(device)
+    context_length = inputs.input_ids.shape[-1]
+    
+    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+    
+    generate_kwargs = dict(
+        inputs=inputs.input_ids,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        streamer=streamer,
+    )
+
+    thread = Thread(target=model.generate, kwargs=generate_kwargs)
+    thread.start()
+    
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer
+
+chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
+
+with gr.Blocks(css=CSS, theme="soft") as demo:
+    gr.HTML(TITLE)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
+    gr.ChatInterface(
+        fn=stream_chat,
+        chatbot=chatbot,
+        fill_height=True,
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False),
+        additional_inputs=[
+            gr.Textbox(
+                value="You are a helpful assistant capable of generating long-form content.",
+                label="System Prompt",
+            ),
+            gr.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=0.5,
+                label="Temperature",
+            ),
+            gr.Slider(
+                minimum=1024,
+                maximum=32768,
+                step=1024,
+                value=32768,
+                label="Max new tokens",
+            ),
+            gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=1.0,
+                label="Top p",
+            ),
+            gr.Slider(
+                minimum=1,
+                maximum=100,
+                step=1,
+                value=50,
+                label="Top k",
+            ),
+        ],
+        examples=[
+            ["Write a 5000-word comprehensive guide on machine learning for beginners."],
+            ["Create a detailed 3000-word business plan for a sustainable energy startup."],
+            ["Compose a 2000-word short story set in a futuristic underwater city."],
+            ["Develop a 4000-word research proposal on the potential effects of climate change on global food security."],
+        ],
+        cache_examples=False,
+    )
+
+if __name__ == "__main__":
+    demo.launch()