UploadApp.py

app.py

@@ -0,0 +1,162 @@
+import torch
+import gradio as gr
+
+from unsloth import FastLanguageModel
+from peft import PeftModel
+
+# =========================
+# Load model once at startup
+# =========================
+
+print("Loading base model...")
+
+base_model, proc = FastLanguageModel.from_pretrained(
+    "unsloth/Qwen3.5-9B",
+    max_seq_length=2048,
+    load_in_4bit=True,  # Recommended unless you have lots of VRAM
+)
+
+tokenizer = proc.tokenizer if hasattr(proc, "tokenizer") else proc
+
+print("Loading LoRA adapter...")
+
+model = PeftModel.from_pretrained(
+    base_model,
+    "XiangJinYu/Qwen3.5-9B-Humanize-DPO-Round2",
+    is_trainable=False,
+)
+
+if hasattr(model, "config") and getattr(model.config, "model_type", "") == "qwen3_5":
+    model.config.model_type = "qwen3"
+
+FastLanguageModel.for_inference(model)
+
+print("Model loaded successfully!")
+
+# =========================
+# Inference function
+# =========================
+
+def humanize_text(
+    text,
+    temperature,
+    top_p,
+    max_tokens,
+):
+    if not text.strip():
+        return ""
+
+    instruction = (
+        "请将下面文本改写得更像自然人写作，"
+        "保持原意与事实，不要加标题或说明。"
+    )
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": f"{instruction}\n\n原文：{text}",
+                }
+            ],
+        }
+    ]
+
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+        enable_thinking=False,
+    )
+
+    inputs = tokenizer(
+        prompt,
+        return_tensors="pt",
+    ).to(model.device)
+
+    with torch.inference_mode():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=int(max_tokens),
+            temperature=float(temperature),
+            top_p=float(top_p),
+            do_sample=True,
+            repetition_penalty=1.1,
+        )
+
+    generated = outputs[0][inputs["input_ids"].shape[1]:]
+
+    result = tokenizer.decode(
+        generated,
+        skip_special_tokens=True,
+    )
+
+    return result.strip()
+
+
+# =========================
+# Gradio UI
+# =========================
+
+with gr.Blocks(title="Qwen Humanizer") as demo:
+    gr.Markdown(
+        """
+        # Qwen Humanizer
+
+        Paste academic, AI-generated, or formal text and rewrite it to sound more natural while preserving meaning.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Input Text",
+                lines=12,
+                placeholder="Paste text here...",
+            )
+
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=1.2,
+                value=0.65,
+                step=0.05,
+                label="Temperature",
+            )
+
+            top_p = gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.9,
+                step=0.05,
+                label="Top P",
+            )
+
+            max_tokens = gr.Slider(
+                minimum=64,
+                maximum=1024,
+                value=512,
+                step=32,
+                label="Max New Tokens",
+            )
+
+            btn = gr.Button("Humanize")
+
+        with gr.Column():
+            output_text = gr.Textbox(
+                label="Humanized Output",
+                lines=12,
+            )
+
+    btn.click(
+        fn=humanize_text,
+        inputs=[
+            input_text,
+            temperature,
+            top_p,
+            max_tokens,
+        ],
+        outputs=output_text,
+    )
+
+demo.launch()