Update app.py

app.py

@@ -1,54 +1,8 @@
-import os
-from threading import Thread
-from typing import Iterator
-
-import gradio as gr
-import spaces
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
-MAX_MAX_NEW_TOKENS = 1024
-DEFAULT_MAX_NEW_TOKENS = 256
-MAX_INPUT_TOKEN_LENGTH = 512
-
-DESCRIPTION = """\
-# OpenELM-3B-Instruct
-
-This Space demonstrates [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) by Apple. Please, check the original model card for details.
-You can see the other models of the OpenELM family [here](https://huggingface.co/apple/OpenELM)
-The following Colab notebooks are available:
-* [OpenELM-3B-Instruct (GPU)](https://gist.github.com/Norod/4f11bb36bea5c548d18f10f9d7ec09b0)
-* [OpenELM-270M (CPU)](https://gist.github.com/Norod/5a311a8e0a774b5c35919913545b7af4)
-
-You might also be interested in checking out Apple's [CoreNet Github page](https://github.com/apple/corenet?tab=readme-ov-file).
-
-If you duplicate this space, make sure you have access to [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
-because this model uses it as a tokenizer.
-
-# Note: Use this model for only for completing sentences and instruction following.
-## While the user interface is a chatbot for convenience, this is an instruction tuned model not fine-tuned for chatbot tasks. As such, the model is not provided a chat history and will complete your text based on the last given prompt only.
-"""
-
-LICENSE = """
-<p/>
-
----
-As a derivative work of [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) by Apple,
-this demo is governed by the original [license](https://huggingface.co/apple/OpenELM-3B-Instruct/blob/main/LICENSE).
-"""
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-
-
-if torch.cuda.is_available():
-    model_id = "apple/OpenELM-3B-Instruct"    
-    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True)
-    tokenizer_id = "meta-llama/Llama-2-7b-hf"
-    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
-    if tokenizer.pad_token == None:
-        tokenizer.pad_token = tokenizer.eos_token
-        tokenizer.pad_token_id = tokenizer.eos_token_id
+# At the top level of your script, after initializing the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
+if tokenizer.pad_token == None:
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.pad_token_id = tokenizer.eos_token_id
 
 @spaces.GPU
 def generate(
@@ -60,91 +14,9 @@ def generate(
     top_k: int = 50,
     repetition_penalty: float = 1.4,
 ) -> Iterator[str]:
+    global tokenizer, model  # Add this line to access global variables
     
     input_ids = tokenizer([message], return_tensors="pt").input_ids
-    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)
-
-    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,
-        pad_token_id = tokenizer.eos_token_id,
-        repetition_penalty=repetition_penalty,
-        no_repeat_ngram_size=5,
-        early_stopping=True,
-    )
-    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(
-    fn=generate,
-    additional_inputs=[
-        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=0.05,
-            value=1.4,
-        ),
-    ],
-    stop_btn=None,
-    examples=[
-        ["A recipe for a chocolate cake:"],
-        ["Can you explain briefly to me what is the Python programming language?"],
-        ["Explain the plot of Cinderella in a sentence."],
-        ["Question: What is the capital of France?\nAnswer:"],
-        ["Question: I am very tired, what should I do?\nAnswer:"],
-    ],
-)
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
-    chat_interface.render()
-    gr.Markdown(LICENSE)
+    # ... rest of the function ...
 
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
+# The rest of your code remains the same