Spaces:
Running
on
Zero
Running
on
Zero
JacobLinCool
commited on
Commit
β’
a5be200
1
Parent(s):
a0347ac
feat: gradio app
Browse files- README.md +3 -1
- app.py +88 -0
- requirements.txt +6 -0
README.md
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---
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title: EssayScoring
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colorFrom: gray
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colorTo: blue
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sdk: gradio
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: EssayScoring
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emoji: π
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colorFrom: gray
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colorTo: blue
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sdk: gradio
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app_file: app.py
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pinned: false
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license: mit
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preload_from_hub:
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- JacobLinCool/IELTS_essay_scoring_safetensors
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from typing import *
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import gradio as gr
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import numpy as np
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import spaces
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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model_name = "JacobLinCool/IELTS_essay_scoring_safetensors"
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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@spaces.GPU
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def grade(question: str, answer: str) -> Tuple[float, float, float, float, float]:
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if len(question) < 30 or len(answer) < 30:
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raise gr.Error("Please enter more than 30 characters")
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text = f"{question} {answer}"
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inputs = tokenizer(
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text, return_tensors="pt", padding=True, truncation=True, max_length=512
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)
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with torch.no_grad():
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outputs = model(**inputs)
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predictions = outputs.logits.squeeze()
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predicted_scores = predictions.numpy()
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normalized_scores = (predicted_scores / predicted_scores.max()) * 9
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rounded_scores = np.round(normalized_scores * 2) / 2
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return tuple(rounded_scores)
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with gr.Blocks() as app:
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gr.Markdown("# Essay Scoring")
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with gr.Row():
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with gr.Column():
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question = gr.Textbox(
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label="Enter the question here",
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placeholder="Write the question here",
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lines=3,
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)
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essay = gr.Textbox(
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label="Enter your essay here",
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placeholder="Write your essay here",
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lines=10,
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)
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btn = gr.Button("Grade Essay", variant="primary")
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with gr.Column():
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task_achievement = gr.Number(label="Task Achievement")
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coherence_cohesion = gr.Number(label="Coherence and Cohesion")
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vocabulary = gr.Number(label="Vocabulary")
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grammar = gr.Number(label="Grammar")
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overall = gr.Number(label="Overall")
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btn.click(
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fn=grade,
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inputs=[question, essay],
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outputs=[task_achievement, coherence_cohesion, vocabulary, grammar, overall],
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)
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gr.Examples(
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[
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[
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"It is important for all towns and cities to have large public spaces such as squares and parks. Do you agree or disagree with this statement?",
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(
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"It is crucial for all metropolitan cities and towns to "
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"have some recreational facilities like parks and squares because of their numerous benefits. A number of "
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"arguments surround my opinion, and I will discuss it in upcoming paragraphs. To commence with, the first "
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"and the foremost merit is that it is beneficial for the health of people because in morning time they can "
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"go for walking as well as in the evenings, also older people can spend their free time with their loved ones, "
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"and they can discuss about their daily happenings. In addition, young people do lot of exercise in parks and "
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"gardens to keep their health fit and healthy, otherwise if there is no park they glue with electronic gadgets "
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"like mobile phones and computers and many more. Furthermore, little children get best place to play, they play "
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"with their friends in parks if any garden or square is not available for kids then they use roads and streets "
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"for playing it can lead to serious incidents. Moreover, parks have some educational value too, in schools, "
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"students learn about environment protection in their studies and teachers can take their pupils to parks because "
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"students can see those pictures so lively which they see in their school books and they know about importance "
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"and protection of trees and flowers. In recapitulate, parks holds immense importance regarding education, health "
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"for people of every society, so government should build parks in every city and town."
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),
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],
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],
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inputs=[question, essay],
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)
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app.launch()
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requirements.txt
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1 |
+
torch
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2 |
+
transformers
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3 |
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accelerate
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spaces
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gradio
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numpy
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