Update app.py

app.py

@@ -1,63 +1,60 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from transformers import pipeline
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
+app_title = "Portuguese Counter Hate Speech Detection (NFAA)"
 
+app_description = """
+This app is the culmination of the kNOwHATE consortium project, which aimed to tackle Online Hate Speech in the Portuguese comunity. It serves as an user-friendly interface to classify text and identify instances of Hate Speech.
+This app leverages state-of-the-art Natural Language Processing models developed in the scope of this project to classify harmful text.
+Select a model from the dropdown menu and input your text to see the classification results. Explore the examples of Hate Speech and Non-Hate Speech offered, and join us in fostering a safer and more respectful online community.
+For more information about the kNOwHATE project and its initiatives, visit our website [here](https://knowhate.eu) and to explore and use these models visit our Hugging Face page [here](https://huggingface.co/knowhate).
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+app_examples = [
+    ["As pessoas tem que perceber que ser 'panasca' não é deixar de ser homem, é deixar de ser humano 😂😂", "knowhate/HateBERTimbau-youtube"],
+    ["Vamo-nos unir para criar um mundo mais inclusivo e tolerante.", "knowhate/HateBERTimbau-twitter"],
+    ["Isso pulhiticos merdosos, continuem a importar lixo, até Portugal deixar de ser Portugal.", "knowhate/HateBERTimbau-yt-tt"],
+    ["Eu admiro muito a coragem e a determinação da minha colega de trabalho.", "knowhate/HateBERTimbau-twitter"],
+    ["Vai pá puta que te pariu seu paneleiro do caralho, virgem ofendida", "knowhate/HateBERTimbau-youtube"],
+    ["O tempo está ensolarado hoje, perfeito para um passeio no parque.", "knowhate/HateBERTimbau-yt-tt"]
+]
+
+model_list = [
+    "knowhate/HateBERTimbau-youtube",
+    "knowhate/HateBERTimbau-twitter",
+    "knowhate/HateBERTimbau-yt-tt",
+]
+
+def predict(text, target, chosen_model):    
+    
+    # Initialize the pipeline with the chosen model
+    model_pipeline = pipeline("text-classification", model=chosen_model)
+    result = model_pipeline(text)
+
+    
+    predicted_label = result[0]['label']
+    predicted_score = result[0]['score']
+
+    non_predicted_label = "Hate Speech" if predicted_label == "Non Hate Speech" else "Non Hate Speech"
+    non_predicted_score = 1 - predicted_score
+
+    scores_dict = {
+        predicted_label: predicted_score,
+        non_predicted_label: non_predicted_score
+    }
+    
+    return scores_dict
+
+inputs = [
+    gr.Textbox(label="Text", value= app_examples[0][0]),
+    gr.Textbox(label="Text", value= app_examples[0][0]),
+    gr.Dropdown(label="Model", choices=model_list, value=model_list[2])
+]
+
+outputs = [
+    gr.Label(label="Result"),
+]
+
+gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title=app_title, 
+             description=app_description, examples=app_examples, theme=gr.themes.Base(primary_hue="red")).launch()