Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the model and tokenizer from Hugging Face Hub
+model_name = "sanzanalora/BanglaBERT_fine-tuned_on_Ben-Sarc"  # Update with your model repo name
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define a function to perform sarcasm detection
+def detect_sarcasm(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    prediction = outputs.logits.argmax(-1).item()
+    return "Sarcastic" if prediction == 1 else "Not Sarcastic"
+
+# Example Bengali sarcasm sentences
+sarcasm_examples = [
+    ["ও হ্যাঁ, তুমি তো অনেক বড় বিজ্ঞানী!"],
+    ["অবশ্যই, এই কাজটা খুবই সহজ ছিল।"],
+    ["তুমি এত দেরি করো না, সবসময়ই সময়মত আসো!"]
+]
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Bengali Sarcasm Detection")
+    gr.Markdown("**Upload your sarcasm detection model and test it with this Gradio interface.**")
+
+    with gr.Tab("Sarcasm Detection"):
+        gr.Examples(
+            sarcasm_examples,
+            inputs=gr.Textbox(label="Enter Bengali Text", lines=2),
+            outputs=gr.Textbox(label="Prediction")
+        )
+
+    input_text = gr.Textbox(label="Enter your Bengali text for sarcasm detection", lines=2)
+    output_text = gr.Textbox(label="Sarcasm Detection Result", lines=2)
+
+    submit_btn = gr.Button("Detect Sarcasm")
+    submit_btn.click(detect_sarcasm, inputs=input_text, outputs=output_text)
+
+demo.launch()