Update app.py

app.py

@@ -1,34 +1,34 @@
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch.nn.functional as F
-
-model_path = "ssocean/NAIP"  # 更换为你的模型路径
-model = AutoModelForSequenceClassification.from_pretrained(model_path, num_labels=1, load_in_8bit=True)
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = model.to(device)
-model.eval()
-
-def predict(title, abstract):
-    # 将标题和摘要处理为一个单一的字符串
-    text = f"Given a certain paper, Title: {title}\nAbstract: {abstract}.\nPredict its normalized academic impact (between 0 and 1):"
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
-    with torch.no_grad():
-        outputs = model(**inputs.to(device))
-    # 应用 Sigmoid 函数来获取概率输出
-    probability = torch.sigmoid(outputs.logits).item()
-    return {"Impact Probability": probability}
-
-# 创建 Gradio 界面
-iface = gr.Interface(
-    fn=predict,
-    inputs=[gr.inputs.Textbox(lines=2, placeholder="Enter Paper Title Here..."),
-            gr.inputs.Textbox(lines=5, placeholder="Enter Paper Abstract Here...")],
-    outputs=[gr.outputs.Label(num_top_classes=1)],
-    title="Newborn Article Impact Prediction based on LLM",
-    description="Predict the normalized academic impact of a paper based on its title and abstract."
-)
-
-iface.launch()
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch.nn.functional as F
+
+model_path = "ssocean/NAIP"  # 更换为你的模型路径
+model = AutoModelForSequenceClassification.from_pretrained(model_path, num_labels=1, load_in_8bit=True)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# model = model.to(device)
+model.eval()
+
+def predict(title, abstract):
+    # 将标题和摘要处理为一个单一的字符串
+    text = f"Given a certain paper, Title: {title}\nAbstract: {abstract}.\nPredict its normalized academic impact (between 0 and 1):"
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs.to(device))
+    # 应用 Sigmoid 函数来获取概率输出
+    probability = torch.sigmoid(outputs.logits).item()
+    return {"Impact Probability": probability}
+
+# 创建 Gradio 界面
+iface = gr.Interface(
+    fn=predict,
+    inputs=[gr.inputs.Textbox(lines=2, placeholder="Enter Paper Title Here..."),
+            gr.inputs.Textbox(lines=5, placeholder="Enter Paper Abstract Here...")],
+    outputs=[gr.outputs.Label(num_top_classes=1)],
+    title="Newborn Article Impact Prediction based on LLM",
+    description="Predict the normalized academic impact of a paper based on its title and abstract."
+)
+
+iface.launch()