Upload app.py

app.py

@@ -1,30 +1,85 @@
-# text generation
+import streamlit as st
+import os
+import google.generativeai as genai
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+# from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+# from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+# from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+import time
+import asyncio
+from dotenv import load_dotenv
+load_dotenv()
 
-from fastapi import FastAPI
-from transformers import pipeline
+# Load environment variables
+# huggingfacehub_api_token = os.getenv("HF_TOKEN")
 
-# create a fastapi instances
+# # Initialize HuggingFace endpoint and LLM
+# repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+# llm_endpoint = HuggingFaceEndpoint(
+#     repo_id=repo_id,
+#     max_length=128,
+#     temperature=0.7,
+#     huggingfacehub_api_token=huggingfacehub_api_token
+# )
 
-app = FastAPI()
+# llm = ChatHuggingFace(llm=llm_endpoint)
 
-# initialize the text generation pipeline
-pipe = pipeline("text2text-generation", model="google/flan-t5-small")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-@app.get("/")
-def home():
-    return {"message":"Hello World!"}
+# Ensure that an event loop exists
+async def initialize_llm():
+    return ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0.5, verbose=True)
 
+llm = asyncio.run(initialize_llm())
 
-# get request '/generate'
+# Function for vector embedding
+def vector_embedding():
+    if "vectors" not in st.session_state:
+        st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+        st.session_state.loader = PyPDFDirectoryLoader("./analysis-pdf")
+        st.session_state.docs = st.session_state.loader.load()
+        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=50)
+        st.session_state.final_docs = st.session_state.text_splitter.split_documents(st.session_state.docs[:30])
+        st.session_state.vectors = FAISS.from_documents(st.session_state.final_docs, st.session_state.embeddings)
 
-@app.get("/generate")
-def generate(text:str):
-    # use the pipeline to generate text from given input text
 
-    output = pipe(text)
+st.title("Gemini RAG DEMO")
 
-    # return the generate text in json response
+prompt = ChatPromptTemplate.from_template(
+    """
+     Answer the questions based on the provided context only.
+     Please provide the most accurate response based on the question.
+     <context>
+     {context}
+     <context>
+     Question: {input}
 
-    return {"output":output[0]['generated_text']}
+    """
+)
 
+question_prompt = st.text_input("Enter Your Question From Documents")
 
+if st.button("Document Embedding"):
+    vector_embedding()
+    st.write("Vector Store DB is Ready!")
+
+if question_prompt:
+    document_chain = create_stuff_documents_chain(llm, prompt)
+    retriever = st.session_state.vectors.as_retriever()
+    retrieval_chain = create_retrieval_chain(retriever, document_chain)
+    start_time = time.process_time()
+    response = retrieval_chain.invoke({"input": question_prompt})
+    print("Response time :", time.process_time() - start_time)
+    st.write(response['answer'])
+
+    with st.expander("Document Similarity Search"):
+        for i, doc in enumerate(response["context"]):
+            st.write(doc.page_content)
+            st.write("---------------------------")