Create app.py

app.py

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+import streamlit as st
+from llama_index import VectorStoreIndex, SimpleDirectoryReader
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from llama_index import LangchainEmbedding, ServiceContext
+from llama_index import StorageContext, load_index_from_storage
+from llama_index import LLMPredictor
+#from transformers import HuggingFaceHub
+from langchain import HuggingFaceHub
+from streamlit.components.v1 import html
+from pathlib import Path
+from time import sleep
+import random
+import string
+
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+import timeit
+
+st.set_page_config(page_title="Open AI Doc-Chat Assistant", layout="wide")
+st.subheader("Open AI Doc-Chat Assistant: Life Enhancing with AI!")
+
+css_file = "main.css"
+with open(css_file) as f:
+    st.markdown("<style>{}</style>".format(f.read()), unsafe_allow_html=True)
+    
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+documents=[]
+
+def generate_random_string(length):
+    letters = string.ascii_lowercase
+    return ''.join(random.choice(letters) for i in range(length))  
+random_string = generate_random_string(20)
+directory_path=random_string
+
+with st.sidebar:
+    st.subheader("Upload your Documents Here: ")
+    pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)
+    if pdf_files:
+        os.makedirs(directory_path)
+        for pdf_file in pdf_files:
+            file_path = os.path.join(directory_path, pdf_file.name)
+            with open(file_path, 'wb') as f:
+                f.write(pdf_file.read())
+            st.success(f"File '{pdf_file.name}' saved successfully.")
+
+try:
+    start_1 = timeit.default_timer() # Start timer
+    st.write(f"QA文档加载开始：{start_1}")
+    documents = SimpleDirectoryReader(directory_path).load_data()
+    end_1 = timeit.default_timer() # Start timer
+    st.write(f"QA文档加载结束：{end_1}")
+    st.write(f"QA文档加载耗时：{end_1 - start_1}")
+except Exception as e:
+    print("waiting for path creation.")
+
+# Load documents from a directory
+#documents = SimpleDirectoryReader('data').load_data()
+    
+start_2 = timeit.default_timer() # Start timer
+st.write(f"向量模型加载开始：{start_2}")
+embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2'))
+end_2 = timeit.default_timer() # Start timer
+st.write(f"向量模型加载加载结束：{end_2}")
+st.write(f"向量模型加载耗时：{end_2 - start_2}")
+
+llm_predictor = LLMPredictor(HuggingFaceHub(repo_id="HuggingFaceH4/starchat-beta", model_kwargs={"min_length":100, "max_new_tokens":1024, "do_sample":True, "temperature":0.2,"top_k":50, "top_p":0.95, "eos_token_id":49155}))
+
+service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
+
+start_3 = timeit.default_timer() # Start timer
+st.write(f"向量库构建开始：{start_3}")
+new_index = VectorStoreIndex.from_documents(
+    documents,
+    service_context=service_context,
+)
+end_3 = timeit.default_timer() # Start timer
+st.write(f"向量库构建结束：{end_3}")
+st.write(f"向量库构建耗时：{end_3 - start_3}")
+
+new_index.storage_context.persist("directory_path")
+
+storage_context = StorageContext.from_defaults(persist_dir="directory_path")
+
+start_4 = timeit.default_timer() # Start timer
+st.write(f"向量库装载开始：{start_4}")
+loadedindex = load_index_from_storage(storage_context=storage_context, service_context=service_context)
+end_4 = timeit.default_timer() # Start timer
+st.write(f"向量库装载结束：{end_4}")
+st.write(f"向量库装载耗时：{end_4 - start_4}")
+
+query_engine = loadedindex.as_query_engine()
+
+user_question = st.text_input("Enter your query here:")
+if user_question !="" and not user_question.strip().isspace() and not user_question == "" and not user_question.strip() == "" and not user_question.isspace():
+    print("user question: "+user_question)
+    with st.spinner("AI Thinking...Please wait a while to Cheers!"):
+        start_5 = timeit.default_timer() # Start timer
+        st.write(f"Query Engine - AI QA开始：{start_5}") 
+        initial_response = query_engine.query(user_question)
+        temp_ai_response=str(initial_response)
+        final_ai_response=temp_ai_response.partition('<|end|>')[0] 
+        print("AI Response:\n"+final_ai_response)
+        st.write("AI Response:\n\n"+final_ai_response)