pidgin-english hate speech detection

app.py

@@ -1,63 +1,38 @@
+from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments
+from transformers import DataCollatorWithPadding
+import torch
+import numpy as np
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-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
-
-"""
-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)",
-        ),
-    ],
+load_tokenizer = RobertaTokenizer.from_pretrained("./saved_model")
+load_model = RobertaForSequenceClassification.from_pretrained("./saved_model")
+
+# Define the prediction function
+def predict(text):
+    inputs = load_tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = load_model(**inputs)
+    logits = outputs.logits
+    prediction = torch.argmax(logits, dim=1).item()
+    return "Hate speech detected" if prediction == 1 else "Hate speech not detected"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs="text",
+    outputs="text",
+    title="Hate Speech Detection System",
+    description="Enter a Pidgin or English text to check if it contains hate speech.",
+        examples = [
+        ["Yoruba men dey craze"],
+        ["Yoruba men are crazy"],
+        ["How una dey"],
+        ["How are you"],
+        ["All these Christians dey mad"],
+        ["All Christians are mad"]
+    ]
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio interface
+iface.launch()

requirements.txt

@@ -1 +1,6 @@
-huggingface_hub==0.22.2
+transformers
+datasets
+torch
+gradio
+numpy
+scikit-learn