create app.py

app.py

@@ -0,0 +1,55 @@
+import os
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import json
+
+os.environ["TRANSFORMERS_CACHE"] = "/tmp"
+
+MODEL_NAME = "s-nlp/roberta-base-formality-ranker"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+def predict_formality(text):
+    # Tokenize input
+    encoding = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    encoding = {k: v.to(device) for k, v in encoding.items()}
+
+    # Predict formality score
+    with torch.no_grad():
+        logits = model(**encoding).logits
+    score = logits.softmax(dim=1)[:, 1].item()
+
+    # Calculate percentages
+    formal_percent = round(score * 100)
+    informal_percent = 100 - formal_percent
+    
+    # Create response in the new format
+    response = {
+        "formality_score": round(score, 3),
+        "formal_percent": formal_percent,
+        "informal_percent": informal_percent,
+        "classification": f"Your speech is {formal_percent}% formal and {informal_percent}% informal."
+    }
+    
+    return response
+
+demo = gr.Interface(
+    fn=predict_formality,
+    inputs=gr.Textbox(label="Enter your text", lines=3),
+    outputs=gr.JSON(label="Formality Analysis"),
+    title="Formality Classifier",
+    description="Enter text to analyze its formality level.",
+    examples=[
+        ["Hello, how are you doing today?"],
+        ["Hey, what's up?"],
+        ["I would like to request your assistance with this matter."]
+    ]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)