Update app.py

app.py

@@ -1,276 +1,92 @@
-from typing import Iterator
-
+import pandas as pd
+import numpy as np
 import gradio as gr
 import torch
-
-from model import get_input_token_length, run
-
-DEFAULT_SYSTEM_PROMPT = """\
-You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\
-"""
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = 4000
-
-DESCRIPTION = """
-# Llama-2 13B Chat
-This Space demonstrates model [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat) by Meta, a Llama 2 model with 13B parameters fine-tuned for chat instructions. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).
-🔎 For more details about the Llama 2 family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/llama2).
-🔨 Looking for an even more powerful model? Check out the large [**70B** model demo](https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI).
-🐇 For a smaller model that you can run on many GPUs, check our [7B model demo](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat).
-"""
-
-LICENSE = """
-<p/>
----
-As a derivate work of [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat) by Meta,
-this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat/blob/main/USE_POLICY.md).
-"""
-
-if not torch.cuda.is_available():
-    DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
-
-
-def clear_and_save_textbox(message: str) -> tuple[str, str]:
-    return '', message
-
-
-def display_input(message: str,
-                  history: list[tuple[str, str]]) -> list[tuple[str, str]]:
-    history.append((message, ''))
-    return history
-
-
-def delete_prev_fn(
-        history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
-    try:
-        message, _ = history.pop()
-    except IndexError:
-        message = ''
-    return history, message or ''
-
-
-def generate(
-    message: str,
-    history_with_input: list[tuple[str, str]],
-    system_prompt: str,
-    max_new_tokens: int,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-) -> Iterator[list[tuple[str, str]]]:
-    if max_new_tokens > MAX_MAX_NEW_TOKENS:
-        raise ValueError
-
-    history = history_with_input[:-1]
-    generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
-    try:
-        first_response = next(generator)
-        yield history + [(message, first_response)]
-    except StopIteration:
-        yield history + [(message, '')]
-    for response in generator:
-        yield history + [(message, response)]
-
-
-def process_example(message: str) -> tuple[str, list[tuple[str, str]]]:
-    generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
-    for x in generator:
-        pass
-    return '', x
-
-
-def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None:
-    input_token_length = get_input_token_length(message, chat_history, system_prompt)
-    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
-        raise gr.Error(f'The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.')
-
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(value='Duplicate Space for private use',
-                       elem_id='duplicate-button')
-
-    with gr.Group():
-        chatbot = gr.Chatbot(label='Chatbot')
-        with gr.Row():
-            textbox = gr.Textbox(
-                container=False,
-                show_label=False,
-                placeholder='Type a message...',
-                scale=10,
-            )
-            submit_button = gr.Button('Submit',
-                                      variant='primary',
-                                      scale=1,
-                                      min_width=0)
-    with gr.Row():
-        retry_button = gr.Button('🔄  Retry', variant='secondary')
-        undo_button = gr.Button('↩️ Undo', variant='secondary')
-        clear_button = gr.Button('🗑️  Clear', variant='secondary')
-
-    saved_input = gr.State()
-
-    with gr.Accordion(label='Advanced options', open=False):
-        system_prompt = gr.Textbox(label='System prompt',
-                                   value=DEFAULT_SYSTEM_PROMPT,
-                                   lines=6)
-        max_new_tokens = gr.Slider(
-            label='Max new tokens',
-            minimum=1,
-            maximum=MAX_MAX_NEW_TOKENS,
-            step=1,
-            value=DEFAULT_MAX_NEW_TOKENS,
-        )
-        temperature = gr.Slider(
-            label='Temperature',
-            minimum=0.1,
-            maximum=4.0,
-            step=0.1,
-            value=1.0,
-        )
-        top_p = gr.Slider(
-            label='Top-p (nucleus sampling)',
-            minimum=0.05,
-            maximum=1.0,
-            step=0.05,
-            value=0.95,
-        )
-        top_k = gr.Slider(
-            label='Top-k',
-            minimum=1,
-            maximum=1000,
-            step=1,
-            value=50,
-        )
-
-    gr.Examples(
-        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'",
-        ],
-        inputs=textbox,
-        outputs=[textbox, chatbot],
-        fn=process_example,
-        cache_examples=True,
-    )
-
-    gr.Markdown(LICENSE)
-
-    textbox.submit(
-        fn=clear_and_save_textbox,
-        inputs=textbox,
-        outputs=[textbox, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=check_input_token_length,
-        inputs=[saved_input, chatbot, system_prompt],
-        api_name=False,
-        queue=False,
-    ).success(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    button_event_preprocess = submit_button.click(
-        fn=clear_and_save_textbox,
-        inputs=textbox,
-        outputs=[textbox, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=check_input_token_length,
-        inputs=[saved_input, chatbot, system_prompt],
-        api_name=False,
-        queue=False,
-    ).success(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    retry_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    undo_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=lambda x: x,
-        inputs=[saved_input],
-        outputs=textbox,
-        api_name=False,
-        queue=False,
-    )
-
-    clear_button.click(
-        fn=lambda: ([], ''),
-        outputs=[chatbot, saved_input],
-        queue=False,
-        api_name=False,
-    )
-
-demo.queue(max_size=20).launch()
-
+from transformers import AutoModelForMultipleChoice, AutoTokenizer
+from huggingface_hub import hf_hub_url, Repository
+model_id="/kaggle/input/deberta-v3-large-hf-weights"
+# Load the model and tokenizer
+
+model = AutoModelForMultipleChoice.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Define the preprocessing function
+def preprocess(sample):
+    first_sentences = [sample["prompt"]] * 5
+    second_sentences = [sample[option] for option in "ABCDE"]
+    tokenized_sentences = tokenizer(first_sentences, second_sentences, truncation=True, padding=True, return_tensors="pt")
+    sample["input_ids"] = tokenized_sentences["input_ids"]
+    sample["attention_mask"] = tokenized_sentences["attention_mask"]
+    return sample
+
+# Define the prediction function
+def predict(data):
+    inputs = torch.stack(data["input_ids"])
+    masks = torch.stack(data["attention_mask"])
+    with torch.no_grad():
+        logits = model(inputs, attention_mask=masks).logits
+    predictions_as_ids = torch.argsort(-logits, dim=1)
+    answers = np.array(list("ABCDE"))[predictions_as_ids.tolist()]
+    return ["".join(i) for i in answers[:, :3]]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Interface.DataType.json,
+    outputs=gr.outputs.Label(num_top_classes=3),
+    live=True,
+    examples=[
+        {"prompt": "This is the prompt", "A": "Option A text", "B": "Option B text", "C": "Option C text", "D": "Option D text", "E": "Option E text"}
+    ],
+    title="LLM Science Exam Demo",
+    description="Enter the prompt and options (A to E) below and get predictions.",
+)
+
+# Run the interface
+iface.launch()
+import pandas as pd
+import numpy as np
+import gradio as gr
+import torch
+from transformers import AutoModelForMultipleChoice, AutoTokenizer
+from huggingface_hub import hf_hub_url, Repository
+
+# Load the model and tokenizer
+model_path = "my_model"
+model = AutoModelForMultipleChoice.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+# Define the preprocessing function
+def preprocess(sample):
+    first_sentences = [sample["prompt"]] * 5
+    second_sentences = [sample[option] for option in "ABCDE"]
+    tokenized_sentences = tokenizer(first_sentences, second_sentences, truncation=True, padding=True, return_tensors="pt")
+    sample["input_ids"] = tokenized_sentences["input_ids"]
+    sample["attention_mask"] = tokenized_sentences["attention_mask"]
+    return sample
+
+# Define the prediction function
+def predict(data):
+    inputs = torch.stack(data["input_ids"])
+    masks = torch.stack(data["attention_mask"])
+    with torch.no_grad():
+        logits = model(inputs, attention_mask=masks).logits
+    predictions_as_ids = torch.argsort(-logits, dim=1)
+    answers = np.array(list("ABCDE"))[predictions_as_ids.tolist()]
+    return ["".join(i) for i in answers[:, :3]]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Interface.DataType.json,
+    outputs=gr.outputs.Label(num_top_classes=3),
+    live=True,
+    examples=[
+        {"prompt": "This is the prompt", "A": "Option A text", "B": "Option B text", "C": "Option C text", "D": "Option D text", "E": "Option E text"}
+    ],
+    title="LLM Science Exam Demo",
+    description="Enter the prompt and options (A to E) below and get predictions.",
+)
+
+# Run the interface
+iface.launch(share=True)