Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+import torch.nn.functional as F
+
+# model yükleme
+model_name = "fc63/gp-model-v3"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+model.eval()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# translate pipeline (multilingual → İngilizce)
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
+
+def predict(text, language):
+    original_text = text
+    if language == "Not English":
+        try:
+            translated = translator(text)[0]["translation_text"]
+        except Exception as e:
+            return f"Translation failed: {e}"
+    else:
+        translated = text
+
+    # model inference
+    inputs = tokenizer(translated, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits, dim=1)
+        pred = torch.argmax(probs, dim=1).item()
+        gender = "Female" if pred == 0 else "Male"
+        confidence = round(probs[0][pred].item() * 100, 1)
+
+    return f"{gender} (Confidence: {confidence}%)"
+
+# interface / arayüz
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Textbox(label="Enter your text here", lines=4, placeholder="Type something..."),
+        gr.Radio(["English", "Not English"], label="Text Language", value="English")
+    ],
+    outputs="text",
+    title="Gender Prediction with DeBERTa",
+    description="Predicts the author's gender from a text. Supports non-English inputs via automatic translation."
+)
+
+demo.launch()