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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+family.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -30,7 +30,7 @@ index = faiss.IndexFlatL2(dimension)
 index.add(np.array(embeddings))
 
 # 构建 LangChain 兼容的 VectorStore
-from langchain.docstore.in_memory import InMemoryDocstore
+from langchain_community.docstore.in_memory import InMemoryDocstore
 from langchain_community.embeddings import HuggingFaceEmbeddings
 
 index_to_docstore_id = {i: str(i) for i in range(len(docs))}
@@ -67,7 +67,6 @@ index = faiss.IndexFlatL2(dimension)
 index.add(np.array(embeddings))
 
 # 构建 LangChain 兼容的 VectorStore
-from langchain.docstore.in_memory import InMemoryDocstore
 from langchain_community.embeddings import HuggingFaceEmbeddings
 
 index_to_docstore_id = {i: str(i) for i in range(len(docs))}
@@ -84,7 +83,7 @@ vectorstore = FAISS(
 retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
 
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from langchain.llms import HuggingFacePipeline
+from langchain_community.llms import HuggingFacePipeline
 from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
 from langchain.prompts import PromptTemplate
@@ -126,6 +125,40 @@ custom_prompt = PromptTemplate(
 
 
 import gradio as gr
+css = """
+    /* 背景图 */
+    .gradio-container {
+        background-image: url("/assets/family.jpg"); /* 相对路径 */
+        background-size: cover;
+        background-position: center;
+        height: 100vh; /* 设置为全屏背景 */
+    }
+       /* 动漫风格按钮 */
+    .gr-button {
+        background-color: #ff69b4;  /* 粉色背景 */
+        border-radius: 50px;  /* 圆形按钮 */
+        font-size: 18px;
+        font-weight: bold;
+        color: white;
+        transition: all 0.3s ease-in-out;
+        padding: 12px 20px;
+    }
+    .gr-button:hover {
+        background-color: #ff1493; /* 鼠标悬停时颜色变化 */
+        transform: scale(1.1);  /* 放大按钮 */
+    }
+     /* 思考动画 */
+    .thinking {
+        animation: spin 2s infinite linear;
+    }
+    @keyframes spin {
+        0% { transform: rotate(0deg); }
+        50% { transform: rotate(180deg); }
+        100% { transform: rotate(360deg); }
+    }
+    ...
+"""
+
 # 构建多轮问答链
 qa_chain = ConversationalRetrievalChain.from_llm(
     llm=llm,
@@ -138,9 +171,15 @@ qa_chain = ConversationalRetrievalChain.from_llm(
 def chat(user_input, history):
     history = history or []
     chat_history = [(q, a) for q, a in history]
-
-    result = qa_chain.invoke({"question": user_input, "chat_history": chat_history})
-    reply = result["answer"]
+    
+    try:
+        # 使用 qa_chain 生成回答（不进行检索，直接参考语料库）
+        result = qa_chain.invoke({"question": user_input, "chat_history": chat_history})
+        reply = result.get("answer", "勾巴,我也不知道!!!")
+    except Exception as e:
+        reply = f"唔sophia累了,可能要犯错了: {str(e)}"
+    
+    # 更新对话历史
     history.append((user_input, reply))
     return history, history