feature:app v1

README.md

@@ -1,12 +1,12 @@
 ---
 title: Portuguese Hate Speech Identifier
-emoji: 🦀
-colorFrom: red
-colorTo: purple
+emoji: 🤗🤬
+colorFrom: yellow
+colorTo: blue
 sdk: gradio
 sdk_version: 4.7.1
 app_file: app.py
-pinned: false
+pinned: true
 license: mit
 ---
 

app.py

@@ -0,0 +1,168 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from collections import Counter
+from scipy.special import softmax
+import plotly.express as px
+
+# Article string
+article_string = "Author: <a href=\"https://huggingface.co/FpOliveira\">Felipe Ramos de Oliveira</a>. Read more about our <a href=\"https://github.com/Silly-Machine/TuPi-Portuguese-Hate-Speech-Dataset\">The Portuguese hate speech dataset (TuPI) </a>."
+
+# App title
+app_title = "Portuguese hate speech identifier (Multiclass) - Identificador de discurso de ódio em português (Multiclasse)"
+
+# App description
+app_description = """
+EN: This application employs multiple natural language models to identify different types of hate speech in portuguese. You have the option to enter your own phrases by filling in the "Text" field or choosing one of the examples provided below.
+\nPT: Esta aplicativo emprega múltiplos modelos de linguagem natural para identificar diferentes tipos de discursos de ódio em português. Você tem a opção de inserir suas próprias frases preenchendo o campo "Text" ou escolhendo um dos exemplos abaixo
+"""
+
+
+# App examples
+app_examples = [
+    ["bom dia flor do dia!!!"],
+    ["o ódio é muito grande no coração da ex-deputada federal joise hasselmann contra a família bolsonaro"],
+    ["mano deus me livre q nojo da porra!🤮🤮🤮🤮🤮"],
+    ["obrigada princesa, porra, tô muito feliz snrsss 🤩🤩🤩❤️"],
+    ["mds mas o viado vir responder meus status falando q a taylor foi racista foi o auge 😂😂"],
+    ["Pra ser minha inimiga no mínimo tem que ter um rostinho bonito e delicado, não se considere minha rival com essa sua cara de cavalo não, feia, cara de traveco, cabeçuda, queixo quadrado 🤣🤣"]
+]
+
+# Output textbox component description
+output_textbox_component_description = """
+EN: This box will display hate speech results based on the average score of multiple models.
+PT: Esta caixa exibirá resultados da classificação de discurso de ódio com base na pontuação média de vários modelos.
+"""
+
+# Output JSON component description
+output_json_component_description = {
+    "breakdown": """
+This box presents a detailed breakdown of the evaluation for each model.
+""",
+    "detalhamento": """
+(Esta caixa apresenta um detalhamento da avaliação para cada modelo.)
+"""
+}
+
+# Hate speech categories
+hate_speech_categories = {
+    0: "ageism",
+    1: "aporophobia",
+    2: "body shame",
+    3: "capacitism",
+    4: "lgbtphobia",
+    5: "political",
+    6: "racism",
+    7: "religious intolerance",
+    8: "misogyny",
+    9: "xenophobia",
+    10: "other",
+    11: "not hate"
+}
+
+# Model list
+model_list = [
+    "FpOliveira/tupi-bert-large-portuguese-cased-multiclass-multilabel",
+    "FpOliveira/tupi-bert-base-portuguese-cased-multiclass-multilabel",
+    "FpOliveira/tupi-gpt2-small-multiclass-multilabel",
+    ]
+
+# User-friendly names for models
+user_friendly_name = {
+    "FpOliveira/tupi-bert-large-portuguese-cased-multiclass-multilabel": "BERTimbau large (TuPi)",
+    "FpOliveira/tupi-bert-base-portuguese-cased-multiclass-multilabel": "BERTimbau base (TuPi)",
+    "FpOliveira/tupi-gpt2-small-multiclass-multilabel":"GPT2 small (TuPi)"
+}
+
+# Reverse mapping for user-friendly names
+reverse_user_friendly_name = {v: k for k, v in user_friendly_name.items()}
+
+# List of user-friendly model names
+user_friendly_name_list = list(user_friendly_name.values())
+
+# Model array
+model_array = []
+
+# Populate model array
+for model_name in model_list:
+    row = {}
+    row["name"] = model_name
+    row["tokenizer"] = AutoTokenizer.from_pretrained(model_name)
+    row["model"] = AutoModelForSequenceClassification.from_pretrained(model_name)
+    model_array.append(row)
+
+
+# Function to find the most frequent element in an array
+def most_frequent(array):
+    occurence_count = Counter(array)
+    return occurence_count.most_common(1)[0][0]
+
+
+# Prediction function
+def predict(s1, chosen_model):
+    # Clear previous figure instance
+    fig = None
+    
+    if not chosen_model:
+        chosen_model = user_friendly_name_list[0]
+    scores = {}
+    full_chosen_model_name = reverse_user_friendly_name[chosen_model]
+    for row in model_array:
+        name = row["name"]
+        if name != full_chosen_model_name:
+            continue
+        else:
+            tokenizer = row["tokenizer"]
+            model = row["model"]
+            model_input = tokenizer(*([s1],), padding=True, return_tensors="pt")
+            with torch.no_grad():
+                output = model(**model_input)
+                logits = output[0][0].detach().numpy()
+                logits = softmax(logits).tolist()
+                break
+
+    # Get the indices of all probabilities
+    all_indices = range(len(logits))
+    
+    # Get the indices of the top two probabilities  
+    top_indices = sorted(range(len(logits)), key=lambda i: logits[i], reverse=True)
+    
+    # Filter out invalid indices
+    valid_indices = [index for index in top_indices if index < len(hate_speech_categories)]
+
+    # Get the categories and probabilities for all classes
+    all_categories = [hate_speech_categories[index] for index in valid_indices]
+    all_probabilities = [logits[index] for index in valid_indices]
+    
+    # Create a bar plot using Plotly
+    fig = px.bar(x=all_categories, y=all_probabilities, labels={'x': 'Categories', 'y': 'Probabilities'},
+                 title="Classes Predicted Probabilities", text=all_probabilities)
+    fig.update_traces(texttemplate='%{text:.4f}', textposition='outside')
+    fig.show()
+    
+    # # Save the figure as HTML
+    # html_figure = fig.to_html()
+    
+    # Get the categories and probabilities for the top two
+    top_category = [hate_speech_categories[index] for index in top_indices]
+    top_probability = [logits[index] for index in top_indices]
+    result = top_category[0], 1-top_probability[1]
+    return result
+
+# Input components
+inputs = [
+    gr.Textbox(label="Text", value=app_examples[0][0]),
+    gr.Dropdown(label="Model", choices=user_friendly_name_list, value=user_friendly_name_list[0])
+]
+
+# Output components
+outputs = [
+    gr.Label(label="Predominant category"),
+    gr.Label(label="Probability"),
+]
+
+# Gradio interface
+gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title=app_title,
+             description=app_description,
+             examples=app_examples,
+             article=article_string).launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch
+gradio
+transformers
+scipy
+plotly