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app.py

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+import gradio as gr
+import regex as re
+import torch
+import nltk
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from nltk.tokenize import sent_tokenize
+import plotly.express as px
+import time
+nltk.download('punkt')
+
+# Define the model and tokenizer
+checkpoint = "sadickam/sdg-classification-bert"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
+
+# Define the function for preprocessing text
+def prep_text(text):
+    clean_sents = []
+    sent_tokens = sent_tokenize(str(text))
+    for sent_token in sent_tokens:
+        word_tokens = [str(word_token).strip().lower() for word_token in sent_token.split()]
+        clean_sents.append(' '.join((word_tokens)))
+    joined = ' '.join(clean_sents).strip(' ')
+    joined = re.sub(r'`', "", joined)
+    joined = re.sub(r'"', "", joined)
+    return joined
+
+# APP INFO
+def app_info():
+    check = """
+    Please go to either the "Single-Text-Prediction" or "Multi-Text-Prediction" tab to analyse your text. 
+    """
+    
+    return check
+
+# Create Gradio interface for single text
+iface1 = gr.Interface(
+    fn=app_info, inputs=None, outputs=['text'], title="General-Infomation",
+    description= '''
+    This app powered by the sgdBERT model (sadickam/sdg-classification-bert) is for automatic classification of text with respect to 
+    the UN Sustainable Development Goals (SDG). Note that 16 out of the 17 SDGs labels are covered. This app is for sustainability 
+    assessment and benchmarking and is not limited to a specific industry. The model powering this app was developed using the 
+    OSDG Community Dataset (OSDG-CD) [Link - https://zenodo.org/record/5550238#.Y8Sd5f5ByF5].
+    
+    This app has two analysis modules summarised below:
+    - Single-Text-Prediction - Analyses text pasted in a text box and return SDG prediction.
+    - Multi-Text-Prediction - Analyses multiple rows of texts in an uploaded CSV file and returns a downloadable CSV file with SDG prediction for each row of text.
+    
+    This app runs on a free server and may therefore not be suitable for analysing large CSV and PDF files. 
+    If you need assistance with analysing large CSV or PDF files, do get in touch using the contact information in the Contact section.
+    
+    <h3>Contact</h3>
+    <p>We would be happy to receive your feedback regarding this app. If you would also like to collaborate with us to explore some use cases for the model 
+    powering this app, we are happy to hear from you.</p>
+    
+    <p>App contact: s.sadick@deakin.edu.au</p>
+    ''')
+
+# SINGLE TEXT
+# Define the prediction function
+def predict_sdg(text):
+    # Preprocess the input text
+    cleaned_text = prep_text(text)
+    # Tokenize the preprocessed text
+    tokenized_text = tokenizer(cleaned_text, return_tensors="pt", truncation=True, max_length=512, padding=True)
+    # Predict
+    text_logits = model(**tokenized_text).logits
+    predictions = torch.softmax(text_logits, dim=1).tolist()[0]
+    # SDG labels
+    label_list = [
+        'GOAL 1: No Poverty',
+        'GOAL 2: Zero Hunger',
+        'GOAL 3: Good Health and Well-being',
+        'GOAL 4: Quality Education',
+        'GOAL 5: Gender Equality',
+        'GOAL 6: Clean Water and Sanitation',
+        'GOAL 7: Affordable and Clean Energy',
+        'GOAL 8: Decent Work and Economic Growth',
+        'GOAL 9: Industry, Innovation and Infrastructure',
+        'GOAL 10: Reduced Inequality',
+        'GOAL 11: Sustainable Cities and Communities',
+        'GOAL 12: Responsible Consumption and Production',
+        'GOAL 13: Climate Action',
+        'GOAL 14: Life Below Water',
+        'GOAL 15: Life on Land',
+        'GOAL 16: Peace, Justice and Strong Institutions'
+    ]
+    # dictionary with label as key and percentage as value
+    pred_dict = dict(zip(label_list, predictions))
+
+    # sort 'pred_dict' by value and index the highest at [0]
+    sorted_preds = sorted(pred_dict.items(), key=lambda x: x[1], reverse=True)
+
+    # Make dataframe for plotly bar chart
+    u, v = zip(*sorted_preds)
+    m = list(u)
+    n = list(v)
+    df2 = pd.DataFrame()
+    df2['SDG'] = m
+    df2['Likelihood'] = n
+
+    # plot graph of predictions
+    fig = px.bar(df2, x="Likelihood", y="SDG", orientation="h")
+
+    fig.update_layout(
+        # barmode='stack', 
+        template='seaborn', font=dict(family="Arial", size=12, color="black"),
+        autosize=True,
+        #width=800,
+        #height=500,
+        xaxis_title="Likelihood of SDG",
+        yaxis_title="Sustainable development goals (SDG)",
+        # legend_title="Topics"
+    )
+
+    fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=12))
+    fig.update_yaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=12))
+    fig.update_annotations(font_size=12)  # this changes y_axis, x_axis and subplot title font sizes
+
+    # Make dataframe for plotly bar chart
+    #df2 = pd.DataFrame(sorted_preds, columns=['SDG', 'Likelihood'])
+
+    # Return the top prediction
+    top_prediction = sorted_preds[0]
+
+    # Return result
+    return {top_prediction[0]: round(top_prediction[1], 3)}, fig
+
+# Create Gradio interface for single text
+iface2 = gr.Interface(fn=predict_sdg, 
+                      inputs=gr.Textbox(lines=7, label="Paste or type text here"), 
+                      outputs=[gr.Label(label="Top SDG Predicted", show_label=True), gr.Plot(label="Likelihood of all SDG", show_label=True)], 
+                      title="Single Text Prediction")
+
+# UPLOAD CSV
+# Define the prediction function
+def predict_sdg_from_csv(file):
+    # Read the CSV file
+    df_docs = pd.read_csv(file)
+    text_list = df_docs["text_inputs"].tolist()
+
+    # SDG labels list
+    label_list = [
+        'GOAL 1: No Poverty',
+        'GOAL 2: Zero Hunger',
+        'GOAL 3: Good Health and Well-being',
+        'GOAL 4: Quality Education',
+        'GOAL 5: Gender Equality',
+        'GOAL 6: Clean Water and Sanitation',
+        'GOAL 7: Affordable and Clean Energy',
+        'GOAL 8: Decent Work and Economic Growth',
+        'GOAL 9: Industry, Innovation and Infrastructure',
+        'GOAL 10: Reduced Inequality',
+        'GOAL 11: Sustainable Cities and Communities',
+        'GOAL 12: Responsible Consumption and Production',
+        'GOAL 13: Climate Action',
+        'GOAL 14: Life Below Water',
+        'GOAL 15: Life on Land',
+        'GOAL 16: Peace, Justice and Strong Institutions'
+    ]
+
+    # Lists for appending predictions
+    predicted_labels = []
+    prediction_score = []
+
+    # Preprocess text and make predictions
+    for text_input in text_list:
+        time.sleep(0.02)  # Sleep to avoid rate limiting
+        cleaned_text = prep_text(text_input)
+        tokenized_text = tokenizer(cleaned_text, return_tensors="pt", truncation=True, max_length=512, padding=True)
+        text_logits = model(**tokenized_text).logits
+        predictions = torch.softmax(text_logits, dim=1).tolist()[0]
+        pred_dict = dict(zip(label_list, predictions))
+        sorted_preds = sorted(pred_dict.items(), key=lambda g: g[1], reverse=True)
+        predicted_labels.append(sorted_preds[0][0])
+        prediction_score.append(sorted_preds[0][1])
+
+    # Append predictions to the DataFrame
+    df_docs['SDG_predicted'] = predicted_labels
+    df_docs['prediction_score'] = prediction_score
+
+    df_docs.to_csv('sdg_predictions.csv')
+    output_csv = gr.File(value='sdg_predictions.csv', visible=True)
+
+    # Create the histogram
+    fig = px.histogram(df_docs, y="SDG_predicted")
+    fig.update_layout(
+        template='seaborn',
+        font=dict(family="Arial", size=12, color="black"),
+        autosize=True,
+        #width=800,
+        #height=500,
+        xaxis_title="SDG counts",
+        yaxis_title="Sustainable development goals (SDG)",
+    )
+    fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=12))
+    fig.update_yaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=12))
+    fig.update_annotations(font_size=12)
+
+    return fig, output_csv
+
+# Define the input component
+file_input = gr.File(label="Upload CSV file here", show_label=True, file_types=[".csv"])
+
+# Create the Gradio interface
+iface3 = gr.Interface(fn=predict_sdg_from_csv, 
+                      inputs= file_input, 
+                      outputs=[gr.Plot(label='Frequency of SDGs', show_label=True), gr.File(label='Download output CSV', show_label=True)], 
+                      title="Multi-text Prediction (CVS)",
+                      description='NOTE: Column to be analysed must be titled ***text_inputs***')
+
+demo = gr.TabbedInterface([iface1, iface2, iface3], ["General-App-Info", "Single-Text-Prediction", "Multi-Text-Prediction (CSV)"])
+
+# Run the interface
+demo.launch()

requirements.txt

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+transformers
+torch
+plotly
+pandas
+numpy
+nltk
+regex
+gradio
+pypdf