Upload app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the model and tokenizer from Hugging Face
+model_name = "TextLabRUET/xlm-r_based_bangla_sentence_classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Mapping predicted class to Bangla sentence types
+class_mapping = {
+    0: "Assertive Sentence (বর্ণনামূলক বাক্য)",
+    1: "Interrogative Sentence (প্রশ্নবোধক বাক্য)",
+    2: "Imperative Sentence (অনুজ্ঞাসূচক বাক্য)",
+    3: "Optative Sentence (প্রার্থনা সূচক বাক্য)",
+    4: "Exclamatory Sentence (বিস্ময়সূচক বাক্য)"
+}
+
+# Function for prediction
+def predict(sentence):
+    inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+    
+    sentence_type = class_mapping.get(predicted_class, "Unknown Sentence Type")
+    return f"Predicted Class: {sentence_type}"
+
+# Create Gradio UI
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a Bangla sentence..."),
+    outputs="text",
+    title="Bangla Sentence Classifier",
+    description=(
+        "This model was trained on a curated Bangla dataset by **TextLab RUET**. "
+        "It classifies Bangla sentences into five distinct categories: Assertive, Interrogative, Imperative, Optative, and Exclamatory "
+        "using the **XLM-R model**. Enter a Bangla sentence below to see how our model interprets it!"
+    ),
+    theme="compact"
+)
+
+# Launch the Gradio app
+iface.launch(share=True)