Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from transformers import RagRetriever, RagTokenizer, RagSequenceForGeneration
+import os
+
+# 从环境变量加载模型
+MODEL_NAME = os.getenv("MODEL_NAME", "facebook/rag-sequence-nq")
+retriever = RagRetriever.from_pretrained(MODEL_NAME, index_name="custom")
+tokenizer = RagTokenizer.from_pretrained(MODEL_NAME)
+model = RagSequenceForGeneration.from_pretrained(MODEL_NAME, retriever=retriever)
+
+def predict(vector):
+    """处理向量输入并返回答案"""
+    try:
+        # 将向量转换为适合检索的格式
+        vector = np.array(vector).reshape(1, -1)
+        
+        # 使用 RAG 进行检索和生成
+        input_dict = tokenizer.prepare_seq2seq_batch(
+            "", 
+            return_tensors="pt"
+        )
+        input_dict["input_ids"] = None  # 使用向量而非文本
+        input_dict["external_vector"] = vector  # 传递自定义向量
+        
+        # 生成答案
+        outputs = model.generate(
+            input_ids=input_dict["input_ids"],
+            external_vector=input_dict["external_vector"],
+            max_length=200
+        )
+        
+        # 解码结果
+        answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+        return answer
+        
+    except Exception as e:
+        return f"处理错误: {str(e)}"
+
+# 创建 Gradio 接口
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Dataframe(headers=["vector"], type="array"),  # 接收向量输入
+    outputs="text",
+    title="电商智能客服",
+    description="输入商品/订单向量获取智能回答"
+)
+
+# 启动应用
+iface.launch(server_name="0.0.0.0", server_port=7860)