space created

.streamlit/config.toml

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+[theme]
+primaryColor="#FF8000"
+#backgroundColor="#FFFFFF"
+#secondaryBackgroundColor="#F0F2F6"
+#textColor="#262730"
+#font="sans serif"

app.py

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+# coding=utf-8
+# Copyright 2023 The GIRT Authors.
+# Lint as: python3
+
+
+# This space is built based on AMR-KELEG/ALDi and cis-lmu/GlotLID space.
+# GIRT Space
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import streamlit as st
+import base64
+
+
+@st.cache_data
+def render_svg(svg):
+    """Renders the given svg string."""
+    b64 = base64.b64encode(svg.encode("utf-8")).decode("utf-8")
+    html = rf'<p align="center"> <img src="data:image/svg+xml;base64,{b64}", width="40%"/> </p>'
+    c = st.container()
+    c.write(html, unsafe_allow_html=True)
+
+
+@st.cache_resource
+def load_model(model_name):
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return model
+
+@st.cache_resource
+def load_tokenizer(model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return tokenizer
+
+with st.spinner(text="Please wait while the model is loading...."):
+
+    model = load_model('nafisehNik/girt-t5-base')
+    tokenizer = load_tokenizer('nafisehNik/girt-t5-base')
+
+
+def compute(sample, num_beams, length_penalty, early_stopping, max_length, min_length):
+
+    inputs = tokenizer(sample, return_tensors="pt").to('cpu')
+
+    outputs = model.generate(
+        **inputs,
+        num_beams=num_beams,
+        num_return_sequences=1,
+        length_penalty=length_penalty,
+        no_repeat_ngram_size=2,
+        early_stopping=early_stopping,
+        max_length=max_length,
+        min_length=min_length).to('cpu')
+
+    generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=False)
+    generated_text = generated_texts[0]
+    
+    replace_dict = {
+        '\n ': '\n',
+        '</s>': '',
+        '<pad> ': '',
+        '<pad>': '',
+        '<unk>': ''
+    }
+    
+    postprocess_text = generated_text
+    for key, value in replace_dict.items():
+        postprocess_text = postprocess_text.replace(key, value)
+
+
+    return postprocess_text
+
+
+st.markdown("[![Duplicate Space](https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14)](https://huggingface.co/spaces/nafisehNik/girt-space?duplicate=true)")
+
+render_svg(open("assets/logo.svg").read())
+
+tab1, tab2 = st.tabs(["Design GitHub Issue Template", "Manual Prompt"])
+
+with tab1:
+    pass
+
+with tab2:
+
+    sent = st.text_input(
+        "Sentence:", placeholder="Enter a prompt.", on_change=None
+    )
+
+    # TODO: Check if this is needed!
+    clicked = st.button("Submit")
+
+    if sent:
+        res = compute(sent, num_beams=2, length_penalty=1.0, early_stopping=True, max_length=300, min_length=20)
+        st.code(res, language="python")
+
+

requirements.txt

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+transformers>=4.35.0,<4.45.0