Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from gradio import Server
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# Load model and tokenizer
+MODEL_NAME = "google-t5/t5-small"
+print("Loading model...")
+tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
+model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
+print("Model loaded!")
+
+
+def translate(text, prompt):
+    """
+    Translate text using T5 with a task prefix.
+    Note: t5-small is trained mainly on English tasks and has limited
+    multilingual translation ability. See note at the bottom.
+    """
+    if not text.strip():
+        return "Please enter some text to translate."
+
+    tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=512,
+            num_beams=4,
+            early_stopping=True,
+        )
+
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+
+
+# ─────────────────────────────────────────────
+# 4. gr.Server — REST API + OPTIONAL SWAGGER UI
+# ─────────────────────────────────────────────
+app = Server(
+    title=free Translator API",
+    summary="free Translator API",
+    version="1.0.0",
+)
+
+# Register as Gradio API endpoints (queued, SSE-streaming capable)
+app.api(translate,        name="translate")        # POST /gradio_api/call/translate
+
+# Optional: plain FastAPI GET health-check route
+@app.get("/health")
+def health():
+    return {"status": "ok", "model": MODEL_ID}
+
+# ─────────────────────────────────────────────
+# 5. LAUNCH
+# ─────────────────────────────────────────────
+app.launch()