Update app.py

app.py

@@ -1,73 +1,63 @@
-import streamlit as st
-import os
-from langchain_groq import ChatGroq
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.prompts import ChatPromptTemplate
-from langchain.chains import create_retrieval_chain
-from langchain_community.vectorstores import FAISS
-from langchain_community.document_loaders import PyPDFDirectoryLoader
-from dotenv import load_dotenv
-import time
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Retrieve the API keys from environment variables
-huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-groq_api_key = os.getenv("GROQ_API_KEY")
-
-# Check if the keys are retrieved correctly
-if huggingfacehub_api_token is None:
-    raise ValueError("HUGGINGFACEHUB_API_TOKEN environment variable is not set")
-if groq_api_key is None:
-    raise ValueError("GROQ_API_KEY environment variable is not set")
-
-# Set environment variables for Hugging Face
-os.environ['HUGGINGFACEHUB_API_TOKEN'] = huggingfacehub_api_token
-
-# Initialize the ChatGroq LLM with the retrieved API key
-llm = ChatGroq(api_key=groq_api_key, model_name="Llama3-8b-8192")
-
-st.title("DataScience Chatgroq With Llama3")
-
-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>
-Questions: {input}
-"""
-)
-
-def vector_embedding():
-    if "vectors" not in st.session_state:
-        st.session_state.embeddings = HuggingFaceEmbeddings()
-        st.session_state.loader = PyPDFDirectoryLoader("./Data_Science")  # Data Ingestion
-        st.session_state.docs = st.session_state.loader.load()  # Document Loading
-        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)  # Chunk Creation
-        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:20])  # Splitting
-        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector HuggingFace embeddings
-
-prompt1 = st.text_input("Enter Your Question From Documents")
-
-if st.button("Documents Embedding"):
-    vector_embedding()
-    st.write("Vector Store DB Is Ready")
-
-if prompt1:
-    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.process_time()
-    response = retrieval_chain.invoke({'input': prompt1})
-    st.write("Response time: ", time.process_time() - start)
-    st.write(response['answer'])
-
-    with st.expander("Document Similarity Search"):
-        for i, doc in enumerate(response["context"]):
-            st.write(doc.page_content)
-            st.write("--------------------------------")
+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+import time
+
+# Retrieve the API keys from environment variables
+huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# Set environment variables for Hugging Face
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = huggingfacehub_api_token
+
+# Initialize the ChatGroq LLM with the retrieved API key
+llm = ChatGroq(api_key=groq_api_key, model_name="Llama3-8b-8192")
+
+st.title("DataScience Chatgroq With Llama3")
+
+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>
+Questions: {input}
+"""
+)
+
+def vector_embedding():
+    if "vectors" not in st.session_state:
+        st.session_state.embeddings = HuggingFaceEmbeddings()
+        st.session_state.loader = PyPDFDirectoryLoader("./Data_Science")  # Data Ingestion
+        st.session_state.docs = st.session_state.loader.load()  # Document Loading
+        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)  # Chunk Creation
+        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:20])  # Splitting
+        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector HuggingFace embeddings
+
+prompt1 = st.text_input("Enter Your Question From Documents")
+
+if st.button("Documents Embedding"):
+    vector_embedding()
+    st.write("Vector Store DB Is Ready")
+
+if prompt1:
+    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.process_time()
+    response = retrieval_chain.invoke({'input': prompt1})
+    st.write("Response time: ", time.process_time() - start)
+    st.write(response['answer'])
+
+    with st.expander("Document Similarity Search"):
+        for i, doc in enumerate(response["context"]):
+            st.write(doc.page_content)
+            st.write("--------------------------------")