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 sys
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+st.set_page_config(page_title="Cheers! 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")
+repo_id=os.getenv("repo_id")
+model_name=os.getenv("model_name")
+
+documents=[]
+wechat_image= "WeChatCode.jpg"
+
+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
+
+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
+)
+
+question = st.text_input("Enter your query here:")
+display_output_text = st.checkbox("Check AI Repsonse", key="key_checkbox", help="Check me to get AI Response.")
+
+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: [[email protected]](mailto:[email protected])")
+    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.")
+        documents = SimpleDirectoryReader(directory_path).load_data()    
+    else:
+        print("waiting for path creation.")
+        sys.exit()
+
+embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name=model_name))
+
+llm = HuggingFaceHub(repo_id=repo_id,
+                     model_kwargs={"min_length":1024,
+                                   "max_new_tokens":5632, "do_sample":True,
+                                   "temperature":0.1,
+                                   "top_k":50,
+                                   "top_p":0.95, "eos_token_id":49155}) 
+
+llm_predictor = LLMPredictor(llm)
+
+service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
+
+new_index = VectorStoreIndex.from_documents(
+    documents,
+    service_context=service_context,
+)
+
+if question !="" and not question.strip().isspace() and not question == "" and not question.strip() == "" and not question.isspace():
+    if display_output_text==True:
+      with st.spinner("AI Thinking...Please wait a while to Cheers!"):
+        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() 
+        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"+str(initial_response))
+    else:
+        print("Check the Checkbox to get AI Response.")
+        sys.exit()          
+else:
+    print("Please enter your question first.")
+    st.stop()