Create app.py

app.py

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+import os
+from dotenv import load_dotenv, find_dotenv
+load_dotenv(find_dotenv())
+from langchain_community.document_loaders import WebBaseLoader
+import google.generativeai as genai
+from langchain import hub
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_google_genai import GoogleGenerativeAI
+from langchain_community.vectorstores import FAISS
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+os.environ['LANGCHAIN_TRACING_V2'] = 'true'
+os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'
+os.environ['LANGCHAIN_PROJECT'] = 'websiteRAG'
+os.environ['LANGCHAIN_API_KEY'] = os.getenv("LANGCHAIN_API_KEY")
+os.getenv('GOOGLE_API_KEY')
+genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
+
+import gradio as gr
+
+def respond(message, chat_history):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vectorstore = FAISS.load_local("./Vector_DB/faiss_index", embeddings, allow_dangerous_deserialization=True)
+    retriever = vectorstore.as_retriever()
+    prompt = hub.pull("rlm/rag-prompt")
+    llm = ChatGoogleGenerativeAI(model="gemini-pro")
+
+    def format_docs(docs):
+        return "\n\n".join(doc.page_content for doc in docs)
+
+    rag_chain = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    
+    result = rag_chain.invoke(message)
+    return result
+css = """
+body {
+    font-family: 'Segoe UI', sans-serif;
+    background-color: #f0f0f0;
+}
+.container {
+    max-width: 1500px;
+    margin: 0 auto;
+    background-color: white;
+    box-shadow: 0 0 10px rgba(0,0,0,0.1);
+    border-radius: 8px;
+    display: flex;
+    flex-direction: column;
+    height: 100px;
+}
+h1 {
+    color: #0078d4;
+    text-align: center;
+    margin-bottom: 10px;
+}
+.description {
+    text-align: center;
+    margin-bottom: 20px;
+}
+.chatbot-container {
+    flex-grow: 1;
+    display: flex;
+    flex-direction: column;
+}
+.footer {
+    text-align: center;
+    font-size: 0.9em;
+    color: #666;
+    margin-top: 20px;
+}
+.footer a {
+    color: #0078d4;
+    text-decoration: none;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML("<div class='container'> <h1>Website RAG</h1> <p class='description'>This website takes a list of URLs and creates a Retrieval-Augmented Generation (RAG) system on top of it. The answers provided are more accurate than previous versions.</p>")
+    with gr.Column(elem_classes="chatbot-container"):
+        chatbot = gr.ChatInterface(
+            respond,
+            analytics_enabled=True,
+            show_progress="full",
+            chatbot=gr.Chatbot(height="70vh"),
+            textbox=gr.Textbox(placeholder="Enter your question here...", container=False),
+
+        )
+    
+    gr.HTML("<div class='footer'>Created by Yash Kavaiya | <a href='https://github.com/Yash-Kavaiya'>GitHub</a> | <a href='https://linkedin.com/in/Yash Kavaiya'>LinkedIn</a></div>")
+    gr.HTML("</div>")
+
+if __name__ == "__main__":
+    demo.launch(share=True)