diff --git a/api.py b/api.py
new file mode 100644
index 0000000..4c93492
--- /dev/null
+++ b/api.py
@@ -0,0 +1,102 @@
+from configs.model_config import *
+from chains.local_doc_qa import LocalDocQA
+import os
+import nltk
+
+import uvicorn
+from fastapi import FastAPI, File, UploadFile
+from pydantic import BaseModel
+from starlette.responses import RedirectResponse
+
+app = FastAPI()
+
+global local_doc_qa, vs_path
+
+nltk.data.path = [os.path.join(os.path.dirname(__file__), "nltk_data")] + nltk.data.path
+
+# return top-k text chunk from vector store
+VECTOR_SEARCH_TOP_K = 10
+
+# LLM input history length
+LLM_HISTORY_LEN = 3
+
+# Show reply with source text from input document
+REPLY_WITH_SOURCE = False
+
+class Query(BaseModel):
+    query: str
+
+@app.get('/')
+async def document():
+    return RedirectResponse(url="/docs")
+
+@app.on_event("startup")
+async def get_local_doc_qa():
+    global local_doc_qa
+    local_doc_qa = LocalDocQA()
+    local_doc_qa.init_cfg(llm_model=LLM_MODEL,
+                          embedding_model=EMBEDDING_MODEL,
+                          embedding_device=EMBEDDING_DEVICE,
+                          llm_history_len=LLM_HISTORY_LEN,
+                          top_k=VECTOR_SEARCH_TOP_K)
+    
+
+@app.post("/file")
+async def upload_file(UserFile: UploadFile=File(...)):
+    global vs_path
+    response = {
+        "msg": None,
+        "status": 0
+    }
+    try:
+        filepath = './content/' + UserFile.filename
+        content = await UserFile.read()
+        # print(UserFile.filename)
+        with open(filepath, 'wb') as f:
+            f.write(content)
+        vs_path = local_doc_qa.init_knowledge_vector_store(filepath)
+        response = {
+            'msg': 'seccessful',
+            'status': 1
+        }
+        
+    except Exception as err:
+        response["message"] = err
+        
+    return response 
+
+@app.post("/qa")
+async def get_answer(UserQuery: Query):
+    response = {
+        "status": 0,
+        "message": "",
+        "answer": None
+    }
+    global vs_path
+    history = []
+    try:
+        resp, history = local_doc_qa.get_knowledge_based_answer(query=UserQuery.query,
+                                                                vs_path=vs_path,
+                                                                chat_history=history)
+        if REPLY_WITH_SOURCE:
+            response["answer"] = resp
+        else:
+            response['answer'] = resp["result"]
+        
+        response["message"] = 'successful'
+        response["status"] = 1
+
+    except Exception as err:
+        response["message"] = err
+        
+    return response
+
+
+if __name__ == "__main__":
+    uvicorn.run(
+        app='api:app', 
+        host='0.0.0.0', 
+        port=8100,
+        reload = True,
+        )
+
diff --git a/webui.py b/webui.py
index 65c21d8..67e6612 100644
--- a/webui.py
+++ b/webui.py
@@ -9,6 +9,10 @@ VECTOR_SEARCH_TOP_K = 6
 
 # LLM input history length
 LLM_HISTORY_LEN = 3
+<<<<<<< HEAD
+=======
+
+>>>>>>> f87a5f5 (fix bug in webui.py)
 
 def get_file_list():
     if not os.path.exists("content"):
@@ -54,7 +58,8 @@ def init_model():
     try:
         local_doc_qa.init_cfg()
         return """模型已成功加载，请选择文件后点击"加载文件"按钮"""
-    except:
+    except Exception as e:
+        print(e)
         return """模型未成功加载，请重新选择后点击"加载模型"按钮"""
 
 
@@ -66,14 +71,15 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, to
                               use_ptuning_v2=use_ptuning_v2,
                               top_k=top_k)
         model_status = """模型已成功重新加载，请选择文件后点击"加载文件"按钮"""
-    except:
+    except Exception as e:
+        print(e)
         model_status = """模型未成功重新加载，请重新选择后点击"加载模型"按钮"""
     return history + [[None, model_status]]
 
 
 
 def get_vector_store(filepath, history):
-    if local_doc_qa.llm and local_doc_qa.llm:
+    if local_doc_qa.llm and local_doc_qa.embeddings:
         vs_path = local_doc_qa.init_knowledge_vector_store(["content/" + filepath])
         if vs_path:
             file_status = "文件已成功加载，请开始提问"