update

app.py

@@ -1,10 +0,0 @@
-import streamlit as st
-from app import docs
-from multiapp import MultiApp
-def main():
-    img = st.sidebar.image("images/huggingface-marathi-roberta.png", width=230)
-    app = MultiApp()
-    app.add_app("Text Classification", docs.app)
-    app.run()
-if __name__ == "__main__":
-    main()

app/docs.py

@@ -1,98 +0,0 @@
-import json
-
-import streamlit as st
-from transformers import AutoTokenizer, RobertaForSequenceClassification, pipeline
-
-with open("config.json") as f:
-    cfg = json.loads(f.read())
-
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def load_model(input_text, model_name_or_path):
-    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
-    model = RobertaForSequenceClassification.from_pretrained(model_name_or_path)
-
-    nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
-    result = nlp(input_text)
-    return result
-
-
-def app():
-    st.title("मराठी Marathi News Classifier")
-
-    st.markdown(
-        "This demo uses the below fine-tuned models for marathi news classification:\
-"
-        "- [IndicNLP Marathi News Classifier](https://huggingface.co/flax-community/mr-indicnlp-classifier) fine-tuned on "
-        "[IndicNLP Marathi News Dataset](https://github.com/ai4bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset)\
-"
-        "> `IndicNLP` model predicts one of these 3 classes - `['lifestyle', 'entertainment', 'sports']`\
-"
-        "- [iNLTK Marathi News Classifier](https://huggingface.co/flax-community/mr-inltk-classifier) fine-tuned on "
-        "[Marathi News Dataset](https://www.kaggle.com/disisbig/marathi-news-dataset)\
-"
-        "> `iNLTK` model predicts one of these 3 classes - `['state', 'entertainment', 'sports']`"
-    )
-
-    classifier = st.sidebar.selectbox("Select a Model", index=0, options=["IndicNLP", "iNLTK"])
-
-    st.sidebar.markdown(
-        "**IndicNLP Classes**\
-"
-        "- lifestyle\
-"
-        "- entertainment\
-"
-        "- sports\
-"
-        "\
-"
-        "**iNLTK Classes**\
-"
-        "- state\
-"
-        "- entertainment\
-"
-        "- sports"
-    )
-
-    sample_texts = [
-        "रोहित शर्माने सरावाला सुरुवात करण्यापूर्वी भारतीय खेळाडूला दिला कानमंत्र, म्हणाला...",
-        "जॉनी लीवर यांनी नम्रता संभेरावला दिलं खास गिफ्ट, अभिनेत्रीने व्यक्त केल्या भावना",
-        "Custom",
-    ]
-    model_name_or_path = cfg["models"][classifier]
-
-    text_to_classify = st.selectbox("Select a Text", options=sample_texts, index=len(sample_texts) - 1)
-
-    if text_to_classify == "Custom":
-        text_to_classify = st.text_input("Enter custom text:")
-
-    predict_button = st.button("Predict")
-
-    if predict_button:
-        with st.spinner("Generating prediction..."):
-            result = load_model(text_to_classify, model_name_or_path)
-
-            st.markdown("## Predicted Label: `{}`".format(result[0]["label"]))
-            st.markdown("## Confidence: `{}`%".format(round(result[0]["score"], 3) * 100))
-
-    st.markdown("- - -")
-    st.markdown(
-        "❓ Can't figure out where to get a sample text other than the predefined ones? ❓\
-"
-        "\
-"
-        "We have provided Marathi newspaper links (section wise) below. Head over to any section of your choice, "
-        "copy any headline and paste below to see if the model is predicting the respective class correctly or not?\
-"
-        "- [entertainment](https://maharashtratimes.com/entertainment/articlelist/19359255.cms)\
-"
-        "- [sports](https://maharashtratimes.com/sports/articlelist/2429056.cms)\
-"
-        "- [lifestyle](https://maharashtratimes.com/lifestyle-news/articlelist/2429025.cms)\
-"
-        "- [state](https://maharashtratimes.com/maharashtra/articlelist/2429066.cms)\
-"
-        "> 📒 NOTE: Both models are not trained on above headlines! Feel free to use any headline from any newspaper"
-    )

multiapp.py

@@ -1,10 +0,0 @@
-import streamlit as st
-class MultiApp:
-    def __init__(self):
-        self.apps = []
-    def add_app(self, title, func):
-        self.apps.append({"title": title, "function": func})
-    def run(self):
-        st.sidebar.header("Tasks")
-        app = st.sidebar.radio("", self.apps, format_func=lambda app: app["title"])
-        app["function"]()