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 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()
+
+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
+
+wechat_image= "WeChatCode.jpg"
+
+st.sidebar.markdown(
+    """
+    <style>
+    .blue-underline {
+        text-decoration: bold;
+        color: blue;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown(
+    """
+    <style>
+        [data-testid=stSidebar] [data-testid=stImage]{
+            text-align: center;
+            display: block;
+            margin-left: auto;
+            margin-right: auto;
+            width: 50%;
+        }
+    </style>
+    """, unsafe_allow_html=True
+)
+
+with st.sidebar:    
+    pdf_files = st.file_uploader("Upload file and start AI Doc-Chat.", type=['pdf'], accept_multiple_files=True)
+    st.write("Disclaimer: This app is for information purpose only. NO liability could be claimed against whoever associated with this app in any manner. User should consult a qualified legal professional for legal advice.")
+    st.sidebar.markdown("Contact: [aichat101@foxmail.com](mailto:aichat101@foxmail.com)")
+    st.sidebar.markdown('WeChat: <span class="blue-underline">pat2win</span>, or scan the code below.', unsafe_allow_html=True)
+    st.image(wechat_image)
+    st.sidebar.markdown('<span class="blue-underline">Life Enhancing with AI.</span>', unsafe_allow_html=True)      
+    st.subheader("Enjoy chatting!")
+    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:
+    documents = SimpleDirectoryReader(directory_path).load_data()
+except Exception as e:
+    print("waiting for path creation.")
+
+embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2'))
+
+llm_predictor = LLMPredictor(HuggingFaceHub(repo_id="HuggingFaceH4/starchat-beta", model_kwargs={"min_length":100, "max_new_tokens":1024, "do_sample":True, "temperature":0.1,"top_k":50, "top_p":0.95, "eos_token_id":49155}))
+
+service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
+
+new_index = VectorStoreIndex.from_documents(
+    documents,
+    service_context=service_context,
+)
+
+new_index.storage_context.persist("directory_path")
+
+storage_context = StorageContext.from_defaults(persist_dir="directory_path")
+
+loadedindex = load_index_from_storage(storage_context=storage_context, service_context=service_context)
+
+query_engine = loadedindex.as_query_engine()
+
+while True:
+    try:
+        question = st.text_input("Enter your query here:")
+        print("Your query:\n"+question)
+        if question.strip().isspace() or question == "" or question.strip() == "" or question.isspace():            
+            break        
+        elif question=="exit":
+            break
+        elif question!="":
+          with st.spinner("AI Thinking...Please wait a while to Cheers!"):
+            initial_response = query_engine.query(question)
+            temp_ai_response=str(initial_response)
+            final_ai_response=temp_ai_response.partition('<|end|>')[0]             
+            st.write("AI Response:\n\n"+final_ai_response)
+    except Exception as e:
+        st.stop()