Add application file

app.py

@@ -1,7 +1,65 @@
+import torch
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-def greet(name):
-    return "Hello " + name + "!!"
+# ===== Model setup =====
+model_path = "protonx-models/protonx-legal-tc"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+MAX_TOKENS = 160
+
+# ===== Inference function =====
+def generate(text):
+    if not text or not text.strip():
+        return ""
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=MAX_TOKENS
+    ).to(device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            num_beams=10,
+            num_return_sequences=1,
+            max_new_tokens=MAX_TOKENS,
+            early_stopping=True
+        )
+
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return decoded
+
+
+# ===== Gradio UI =====
+with gr.Blocks(title="ProtonX Legal Text Generation") as demo:
+    gr.Markdown("## 🧾 ProtonX Legal Text Generation")
+
+    input_text = gr.Textbox(
+        label="Input text",
+        placeholder="Nhập nội dung pháp lý...",
+        lines=6
+    )
+
+    submit_btn = gr.Button("Submit")
+
+    output_text = gr.Textbox(
+        label="Output",
+        lines=6
+    )
+
+    submit_btn.click(
+        fn=generate,
+        inputs=input_text,
+        outputs=output_text
+    )
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+torch
+transformers
+gradio