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()
+
+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")
+
+# Load documents from a directory
+documents = SimpleDirectoryReader('data').load_data()
+
+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":0.2,"top_k":50, "top_p":0.95, "eos_token_id":49155}))
+
+service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
+
+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)
+
+new_index = VectorStoreIndex.from_documents(
+    documents,
+    service_context=service_context,
+)
+
+new_index.storage_context.persist("random_string")
+
+storage_context = StorageContext.from_defaults(persist_dir="random_string")
+
+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():
+            st.write("Query Empty. Please enter valid query first.")
+            break
+        elif question == "":
+#            st.write("Query Empty. Please enter valid query first.")
+            break
+        elif question.strip() == "":
+            st.write("Query Empty. Please enter valid query first.")
+            break
+        elif question.isspace():
+            st.write("Query Empty. Please enter valid query first.")
+            break
+        elif question=="exit":
+            break
+        elif question!="":
+            initial_response = query_engine.query(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)
+    except Exception as e:
+        st.stop()