Create app.py

app.py

@@ -0,0 +1,33 @@
+import os
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+
+MODEL_ID = os.getenv("MODEL_ID", "cardiffnlp/twitter-xlm-roberta-base-sentiment")
+LABEL_MAP = {0: "negative", 1: "neutral", 2: "positive"}  # modelin etiket sirasi
+
+# modeli ve tokenizer'i bir kez yukle
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, framework="pt", device=-1)
+
+def analyze(text: str):
+    text = (text or "").strip()
+    if not text:
+        return {"label": "neutral", "score": 1.0}
+    scores = pipe(text)[0]  # [{label: "...", score: ...}, ...]
+    max_idx = max(range(len(scores)), key=lambda i: scores[i]["score"])
+    label = LABEL_MAP.get(max_idx, scores[max_idx]["label"]).lower()
+    score = float(scores[max_idx]["score"])
+    return {"label": label, "score": round(score, 4)}
+
+demo = gr.Interface(
+    fn=analyze,
+    inputs=gr.Textbox(lines=3, placeholder="type a message..."),
+    outputs=gr.JSON(),
+    title="chat sentiment api",
+    description="returns json: {label: positive|neutral|negative, score: 0..1}",
+)
+demo.api_name = "analyze"  # endpoint: /api/predict/analyze
+
+if __name__ == "__main__":
+    demo.launch()