Created a github action workflow

.github/workflows/main.yaml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab 
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: false
+
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://Suraj-Yadav:$HF_TOKEN@huggingface.co/spaces/Suraj-Yadav/Search_Engine_LLM main
+
+           # git push https://HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/HF_USERNAME/SPACE_NAME main
+
+            

app.py

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+import streamlit as st
+from langchain_groq import ChatGroq
+from langchain_community.utilities import ArxivAPIWrapper,WikipediaAPIWrapper
+from langchain_community.tools import ArxivQueryRun,WikipediaQueryRun,DuckDuckGoSearchRun
+from langchain.agents import initialize_agent,AgentType
+from langchain.callbacks import StreamlitCallbackHandler
+import os
+from dotenv import load_dotenv
+
+## Arxiv and wikipedia Tools
+arxiv_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+arxiv=ArxivQueryRun(api_wrapper=arxiv_wrapper)
+
+api_wrapper=WikipediaAPIWrapper(top_k_results=1,doc_content_chars_max=200)
+wiki=WikipediaQueryRun(api_wrapper=api_wrapper)
+
+search=DuckDuckGoSearchRun(name="Search")
+
+
+st.title("🔎 LangChain - Chat with search")
+"""
+In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
+Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
+"""
+
+## Sidebar for settings
+st.sidebar.title("Settings")
+api_key=st.sidebar.text_input("Enter your Groq API Key:",type="password")
+
+if "messages" not in st.session_state:
+    st.session_state['messages'] = [
+        {"role":"assisstant","content":"Hi,I'm a chatbot who can search the web. How can I help you?"}
+    ]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg['content'])
+
+if prompt:=st.chat_input(placeholder="What is machine learning?"):
+    st.session_state.messages.append({"role":"user","content":prompt})
+    st.chat_message("user").write(prompt)
+
+    llm=ChatGroq(groq_api_key=api_key,model_name="Llama3-8b-8192",streaming=True)
+    tools=[search,arxiv,wiki]
+
+    search_agent=initialize_agent(tools,llm,agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,handling_parsing_errors=False)
+
+    with st.chat_message("assistant"):
+        st_cb=StreamlitCallbackHandler(st.container(),expand_new_thoughts=False)
+        response=search_agent.run(st.session_state.messages,callbacks=[st_cb])
+        st.session_state.messages.append({'role':'assistant',"content":response})
+        st.write(response)

requirements.txt

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+langchain
+python-dotenv
+ipykernel
+langchain-community
+pypdf
+bs4
+arxiv
+pymupdf
+wikipedia
+langchain-text-splitters
+langchain-openai
+chromadb
+sentence_transformers
+langchain_huggingface
+faiss-cpu
+langchain_chroma
+duckdb
+pandas
+openai
+langchain-groq
+duckduckgo_search==5.3.1b1
+pymupdf
+arxiv
+wikipedia
+mysql-connector-python
+SQLAlchemy
+validators==0.28.1
+youtube_transcript_api
+unstructured
+pytube
+numexpr
+huggingface_hub