Update app.py

app.py

@@ -5,7 +5,7 @@ from typing import Iterator
 import gradio as gr
 import spaces
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 
 MAX_MAX_NEW_TOKENS = 2048
 DEFAULT_MAX_NEW_TOKENS = 1024
@@ -19,15 +19,22 @@ This Space demonstrates [L-MChat](https://huggingface.co/collections/Artples/l-m
 if not torch.cuda.is_available():
     DESCRIPTION += "\n<p>Running on CPU! This demo does not work on CPU.</p>"
 
-model_options = {
+# Dictionary to manage model details
+model_details = {
     "Fast-Model": "Artples/L-MChat-Small",
     "Quality-Model": "Artples/L-MChat-7b"
 }
 
+# Initialize models and tokenizers based on availability
+models = {name: AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") for name, model_id in model_details.items()}
+tokenizers = {name: AutoTokenizer.from_pretrained(model_id) for name, model_id in model_details.items()}
+for tokenizer in tokenizers.values():
+    tokenizer.use_default_system_prompt = False
+
 @spaces.GPU(enable_queue=True, duration=90)
 def generate(
-    message: str,
     model_choice: str,
+    message: str,
     chat_history: list[tuple[str, str]],
     system_prompt: str,
     max_new_tokens: int = 1024,
@@ -36,23 +43,97 @@ def generate(
     top_k: int = 50,
     repetition_penalty: float = 1.2,
 ) -> Iterator[str]:
-    # Your existing function implementation...
-    pass
+    model = models[model_choice]
+    tokenizer = tokenizers[model_choice]
+
+    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", add_generation_prompt=True)
+    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)
 
-chat_interface = gr.Interface(
+    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,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+chat_interface = gr.ChatInterface(
+    theme='ehristoforu/RE_Theme',
     fn=generate,
-    inputs=[
-        gr.Textbox(lines=2, placeholder="Type your message here..."),
-        gr.Dropdown(label="Choose Model", choices=list(model_options.keys())),
-        chat_history,  # Updated to include state without label
-        gr.Textbox(label="System Prompt", lines=6, placeholder="Enter system prompt if any..."),
-        gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS),
-        # More inputs as previously defined
+    additional_inputs=[
+        gr.Textbox(label="System prompt", lines=6),
+        gr.Dropdown(label="Model Choice", choices=list(model_details.keys()), value="Quality-Model"),
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
+            minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.9,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step.05,
+            value=1.2,
+        ),
+    ],
+    stop_btn=None,
+    examples=[
+        ["Hello there! How are you doing?"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["Explain the plot of Cinderella in a sentence."],
+        ["How many hours does it take a man to eat a Helicopter?"],
+        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
     ],
-    outputs=[gr.Textbox(label="Response")],
-    theme="default",
-    description=DESCRIPTION
 )
 
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
+
 if __name__ == "__main__":
-    chat_interface.launch()
+    demo.queue(max_size=20).launch()