Update app.py

app.py

@@ -1,64 +1,51 @@
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# 模型名称
+model_name = "baidu/ERNIE-4.5-0.3B-PT"
 
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# 加载 tokenizer 和模型（首次运行可能较慢）
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    torch_dtype=torch.float32,
+    device_map="auto"
+)
+embedding_layer = model.get_input_embeddings()
+
+# 提取句子的平均 embedding
+def get_sentence_embedding(text):
+    inputs = tokenizer(text, return_tensors="pt", add_special_tokens=True)
+    input_ids = inputs["input_ids"]
+    with torch.no_grad():
+        embeddings = embedding_layer(input_ids)  # shape: [1, seq_len, hidden_size]
+        sentence_embedding = embeddings.mean(dim=1)  # shape: [1, hidden_size]
+    return sentence_embedding
+
+# Gradio 回调函数
+def calculate_similarity(sentence1, sentence2):
+    emb1 = get_sentence_embedding(sentence1)
+    emb2 = get_sentence_embedding(sentence2)
+    similarity = F.cosine_similarity(emb1, emb2).item()
+    return f"Similarity: {similarity:.4f}"
+
+# Gradio 界面
+title = "Calculate two sentences's similarity by ERNIE 4.5-0.3B's embedding layer"
+demo = gr.Interface(
+    fn=calculate_similarity,
+    inputs=[
+        gr.Textbox(label="Sentence 1", placeholder="我爱北京"),
+        gr.Textbox(label="Sentence 2", placeholder="我爱上海")
     ],
+    outputs=gr.Textbox(label="Similarity"),
+    title=title,
+    description="This app uses the embedding layer of Baidu ERNIE-4.5-0.3B-PT model to compute the cosine similarity between two sentences.",
 )
 
-
+# 启动 Gradio app
 if __name__ == "__main__":
     demo.launch()
+