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.python-version

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+3.10

.streamlit/config.toml

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+[server]
+enableCORS=false
+port = 8501
+
+[theme]
+# Primary accent for interactive elements
+primaryColor = '#7792E3'
+# Background color for the main content area
+backgroundColor = '#000319'
+# Background color for sidebar and most interactive widgets
+secondaryBackgroundColor = '#52968e'
+# Color used for almost all text
+textColor = '#FFFFFF'
+# Font family for all text in the app, except code blocks
+# Accepted values (serif | sans serif | monospace) 
+# Default: "sans serif"
+font = "sans serif"

railway.json

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+{
+    "$schema": "https://railway.app/railway.schema.json",
+    "build": {
+        "builder": "NIXPACKS"
+    },
+    "deploy": {
+        "startCommand": "streamlit run streamlit_app.py  --server.headless true --server.address 0.0.0.0 --server.port $PORT --server.fileWatcherType none --browser.gatherUsageStats false --client.toolbarMode minimal"
+    }
+}

requirements.txt

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+numpy==1.26.4
+streamlit
+transformers
+torch
+fasttext-langdetect
+python-iso639
+scikit-learn
+numpy==1.26.4
+tensorflow

streamlit_app.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import re, spacy
+from time import time
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.layers import Input, Embedding, LSTM, Concatenate, TimeDistributed, Dense
+from tensorflow.keras.models import Model
+from tensorflow.keras.callbacks import EarlyStopping
+import warnings
+warnings.filterwarnings('ignore')
+
+import pickle
+import streamlit as st
+from ftlangdetect import detect
+import iso639
+import streamlit.components.v1 as components
+import os
+gpt2_tokenizer = None
+gpt2_model = None
+from transformers import (
+    # GPT2Config,
+    #                       GPT2Tokenizer,
+    #                       GPT2Model,
+                          BertTokenizer, 
+                          BertModel)
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+bert_tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')
+bert_model = BertModel.from_pretrained('bert-base-multilingual-uncased')
+class_names = {0:'sadness', 1:'joy', 2:'love', 3:'anger', 4:'fear', 5:'surprise'}
+import os
+gpt2_tokenizer = None
+gpt2_model = None
+# gpt2_model = GPT2Model.from_pretrained("gpt2")
+# gpt2_tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+# gpt2_tokenizer.padding_side = "left"
+# gpt2_tokenizer.pad_token = gpt2_tokenizer.eos_token
+# Define preprocessing function with smaller max length
+def tokenize_sample(texts, tokenizer="bert"):
+    if tokenizer == "gpt2":
+        return gpt2_tokenizer(texts, padding="max_length", truncation=True, return_tensors='pt', max_length=128)
+    return bert_tokenizer(texts, padding=True, truncation=True, return_tensors='pt', max_length=128)
+def get_embeddings(text, model_type="bert"):
+    tokenized_text = tokenize_sample(text, model_type)
+    if model_type =="gpt2":
+        outputs = gpt2_model(**tokenized_text)
+    else:
+        outputs = bert_model(**tokenized_text)
+    embeddings = outputs.last_hidden_state[:, 0, :].detach().numpy()  # Get the embeddings for [CLS] token
+    return embeddings
+
+# path_to_models = ".."
+path_to_models = os.environ['RAILWAY_VOLUME_MOUNT_PATH']+"/storage"
+emotion_classifier_map={
+"Naive Bayes":f"{path_to_models}/models/naive_bayes_model.sav",
+"Logistic Regression":f"{path_to_models}/models/logistic_regression_model.sav",
+"KNN":f"{path_to_models}/models/knn_model.sav",
+"KMeans":f"{path_to_models}/models/kmeans_model.sav",
+"SVM":f"{path_to_models}/models/svm_model.sav",
+"Decision Tree":f"{path_to_models}/models/decision_tree_model.sav",
+"Random Forest":f"{path_to_models}/models/random_forest_model.sav"
+}
+summarizer_map={
+    "Bengali":f"{path_to_models}/models/bengali_summarization_model.sav",
+}
+# print(os.listdir())
+# print(os.environ["RAILWAY_VOLUME_MOUNT_PATH"])
+# print(os.listdir(os.environ["RAILWAY_VOLUME_MOUNT_PATH"]+"/storage"))
+summarizer_models=dict()
+for i in summarizer_map:
+    with open(summarizer_map[i], 'rb') as file:
+        summarizer_models[i] = pickle.load(file)
+emotion_classfier_models=dict()
+for i in emotion_classifier_map:
+    with open(emotion_classifier_map[i], 'rb') as file:
+        emotion_classfier_models[i] = pickle.load(file)
+def get_emotion_prediction(input, model_name):
+    if model_name in emotion_classfier_models:
+        return class_names[emotion_classfier_models[model_name].predict(get_embeddings(input))[0]]
+    else:
+        raise ValueError("Model type should be of the types: {}".format(", ".join(list(emotion_classfier_models.keys()))))
+    
+def decode_sequence(input_seq, max_summary_len, encoder_model, decoder_model, target_word_index, reverse_target_word_index):
+    # Encode the input as state vectors.
+    e_out, e_h, e_c = encoder_model.predict(input_seq)
+
+    # Generate empty target sequence of length 1.
+    target_seq = np.zeros((1,1))
+
+    # Populate the first word of target sequence with the start word.
+    target_seq[0, 0] = target_word_index['sostok']
+
+    stop_condition = False
+    decoded_sentence = ''
+    while not stop_condition:
+
+        output_tokens, h, c = decoder_model.predict([target_seq] + [e_out, e_h, e_c])
+
+        # Sample a token
+        sampled_token_index = np.argmax(output_tokens[0, -1, :])
+        sampled_token = reverse_target_word_index[sampled_token_index]
+
+        if(sampled_token!='eostok'):
+            decoded_sentence += ' '+sampled_token
+
+        # Exit condition: either hit max length or find stop word.
+        if (sampled_token == 'eostok'  or len(decoded_sentence.split()) >= (max_summary_len-1)):
+            stop_condition = True
+
+        # Update the target sequence (of length 1).
+        target_seq = np.zeros((1,1))
+        target_seq[0, 0] = sampled_token_index
+
+        # Update internal states
+        e_h, e_c = h, c
+
+    return decoded_sentence
+
+def summarize_text(text, x_tokenizer, max_text_len, max_summary_len, encoder_model, decoder_model, target_word_index, reverse_target_word_index):
+    tokenized_sentence = pad_sequences(x_tokenizer.texts_to_sequences([text]),  maxlen=max_text_len, padding='post')[0]
+    return decode_sequence(tokenized_sentence.reshape(1,max_text_len), max_summary_len, encoder_model, decoder_model, target_word_index, reverse_target_word_index)
+
+def main():
+    list_of_tabs = st.tabs(["Indic Multilingual Text Summarizer", "Indic Multilingual Emotion Detection"])
+    # Title of the web app
+    with list_of_tabs[0]:
+        st.title('Indic Multilingual Text Summarizer')
+        # print(os.listdir())
+        # print(os.environ["RAILWAY_VOLUME_MOUNT_PATH"])
+        # print(os.listdir(os.environ["RAILWAY_VOLUME_MOUNT_PATH"]))
+        
+        # Input text from the user
+        input_sentence_emotion = st.text_input('Enter a sentence', key="summarize")
+
+        # Model selection
+        # model_option = st.selectbox('Select the model', list(models.keys()))
+        # Result initialization
+        result = None
+        error = None
+        langlist = {"bn": "Bengali"}
+        # Prediction button
+        if st.button('Summarize'):
+            lang = detect(text=input_sentence_emotion, low_memory=False)['lang']
+            if lang in langlist:
+                result = summarize_text(input_sentence_emotion, summarizer_models[langlist[lang]]["x_tokenizer"], summarizer_models[langlist[lang]]["max_text_len"],summarizer_models[langlist[lang]]['max_summary_len'], summarizer_models[langlist[lang]]['encoder_model'], summarizer_models[langlist[lang]]['decoder_model'], summarizer_models[langlist[lang]]['target_word_index'], summarizer_models[langlist[lang]]['reverse_target_word_index']).replace("start ", "").replace(" end", "")
+            else:
+                error = f"{iso639.Language.from_part1(lang).name} is not supported.\n List of supported languages: {', '.join(langlist.values())}"
+        st.markdown(f"Current language support: Bengali")
+        # Display the result
+        if result:
+            st.success(f'Summary: {result}')
+        if error:
+            st.error(f'Error: {error}')
+        # Credits
+        # Credits
+        
+    
+    with list_of_tabs[1]:
+        st.title('Indic Multilingual Emotion Detection')
+        # print(os.listdir())
+        # print(os.environ["RAILWAY_VOLUME_MOUNT_PATH"])
+        # print(os.listdir(os.environ["RAILWAY_VOLUME_MOUNT_PATH"]))
+        
+        # Input text from the user
+        input_sentence_emotion = st.text_input('Enter a sentence', key="emotion")
+
+        # Model selection
+        model_option = st.selectbox('Select the model', list(emotion_classfier_models.keys()))
+
+        # Result initialization
+        result = None
+        error = None
+        langlist = {"hi": "Hindi"}
+        # Prediction button
+        if st.button('Predict Emotion'):
+            lang = detect(text=input_sentence_emotion, low_memory=False)['lang']
+            if lang in langlist:
+                result = get_emotion_prediction(input_sentence_emotion, model_option)
+            else:
+                error = f"{iso639.Language.from_part1(lang).name} is not supported.\n List of supported languages: {', '.join(langlist.values())}"
+        st.markdown(f"Current language support: Hindi")
+        # Display the result
+        if result:
+            st.success(f'Prediction: {result}')
+        if error:
+            st.error(f'Error: {error}')
+        # Credits
+        # Credits
+    st.markdown("---")  # Separator
+    st.markdown("""## Contributors  
+- Bishwaraj Paul  
+**Role** Intern   
+**Email:** bishwaraj.paul98@gmail.com  
+- Dr. Sahinur Rahman Laskar  
+**Role:** Mentor  
+Assistant Professor  
+School of Computer Science, UPES, Dehradun, India  
+**Email:** sahinurlaskar.nits@gmail.com / sahinur.laskar@ddn.upes.ac.in""")
+    footer = """<style>
+    .footer-text{
+    -webkit-text-size-adjust: 100%;
+    -webkit-tap-highlight-color: transparent;
+    --blue: #007bff;
+    --indigo: #6610f2;
+    --purple: #6f42c1;
+    --pink: #e83e8c;
+    --red: #dc3545;
+    --orange: #fd7e14;
+    --yellow: #ffc107;
+    --green: #28a745;
+    --teal: #20c997;
+    --cyan: #17a2b8;
+    --white: #fff;
+    --gray: #6c757d;
+    --gray-dark: #343a40;
+    --primary: #007bff;
+    --secondary: #6c757d;
+    --success: #28a745;
+    --info: #17a2b8;
+    --warning: #ffc107;
+    --danger: #dc3545;
+    --light: #f8f9fa;
+    --dark: #343a40;
+    --breakpoint-xs: 0;
+    --breakpoint-sm: 576px;
+    --breakpoint-md: 768px;
+    --breakpoint-lg: 992px;
+    --breakpoint-xl: 1200px;
+    --font-family-sans-serif: -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";
+    --font-family-monospace: SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;
+    font-size: 16px;
+    font-weight: 400;
+    line-height: 24px;
+    letter-spacing: 1px;
+    font-family: 'Raleway', sans-serif;
+    color: #666;
+    box-sizing: border-box;
+    text-align: center!important;
+    }
+    @media (min-width: 576px) {
+        .col-sm-12 {
+            -webkit-box-flex: 0;
+            -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+            max-width: 100%;
+        }
+    }
+    .row {
+        display: -webkit-box;
+        display: -ms-flexbox;
+        display: flex;
+        -ms-flex-wrap: wrap;
+        flex-wrap: wrap;
+        margin-right: -15px;
+        margin-left: -15px;
+    }
+    @media (min-width: 1200px) {
+        .container {
+            max-width: 1140px;
+        }
+    }
+    @media (min-width: 992px) {
+        .container {
+            max-width: 960px;
+        }
+    }
+    @media (min-width: 768px) {
+        .container {
+            max-width: 720px;
+        }
+    }
+    @media (min-width: 576px) {
+        .container {
+            max-width: 540px;
+        }
+    }
+    .container {
+        width: 100%;
+        padding-right: 15px;
+        padding-left: 15px;
+        margin-right: auto;
+        margin-left: auto;
+    }
+    .footer-bottom-area {
+        padding: 30px 0;
+        display: block;
+        box-sizing: border-box;
+    }
+    .footer-bottom-bg {
+        background: #222;
+    }
+    </style>
+    <footer class="footer-bottom-area footer-bottom-bg">
+        <div class="container">
+            <div class="row">
+                <div class="col-sm-12">
+                    <div class="footer-text">
+                        <p style="color: white; font-style: sans-serif;"><span>Bahash Private Limited</span> ©2024 - All Right Reserved.</p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </footer>
+    """
+    components.html(footer)
+    # Handling query parameters
+    query = st.experimental_get_query_params()
+    try:
+        ## Look-up the tab from the query
+        index_tab = query["tab"][0]
+        ## Click on that tab
+        js = f"""
+        <script>
+            var tab = window.parent.document.getElementById('tabs-bui2-tab-{index_tab}');
+            tab.click();
+        </script>
+        """
+        st.components.v1.html(js)
+
+    except ValueError:
+        ## Do nothing if the query parameter does not correspond to any of the tabs
+        pass
+
+if __name__ == '__main__':
+    main()

test.js

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+< !doctype html > <html lang="en"><head><meta charset="UTF-8" /><meta name="viewport" content="width=device-width,initial-scale=1,shrink-to-fit=no" /><link rel="shortcut icon" href="./favicon.png" /><link rel="preload" href="./static/media/SourceSansPro-Regular.0d69e5ff5e92ac64a0c9.woff2" as="font" type="font/woff2" crossorigin><link rel="preload" href="./static/media/SourceSansPro-SemiBold.abed79cd0df1827e18cf.woff2" as="font" type="font/woff2" crossorigin><link rel="preload" href="./static/media/SourceSansPro-Bold.118dea98980e20a81ced.woff2" as="font" type="font/woff2" crossorigin><title>Streamlit</title><script>window.prerenderReady=!1</script><script defer="defer" src="./static/js/main.d55f6a3c.js"></script><link href="./static/css/main.29bca1b5.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>