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app.py

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+from dotenv import load_dotenv
+load_dotenv()
+
+import streamlit as st 
+import os 
+import google.generativeai as genai 
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+
+# Now we will load Gemini Pro model and get response
+model = genai.GenerativeModel("gemini-pro")
+chat = model.start_chat(history=[])
+
+def get_gemini_response(question):
+    response = chat.send_message(question, stream=True)
+    return response 
+
+# Now we will build  our streamlit app
+st.set_page_config(page_title="Q&A with LLM")
+
+# st.set_page_config(
+#     page_title="Q&A with LLM",
+#     page_icon=":shark:",
+#     layout="centered",  # Can be "wide" or "centered"
+#     initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
+#     background_image="./Q&A.avif",  # URL or local file path
+# )
+
+
+st.header("LLM-based Q&A Application")
+
+# Initializing the session state for chat history if it does not exist
+if "chat_history" not in st.session_state:
+    st.session_state["chat_history"] = [] 
+    
+    
+input = st.text_input("Input:", key="input")
+submit = st.button("Ask me your Question!")
+
+if submit and input:
+    response = get_gemini_response(input)
+    # Adding user query and Bot response to session chat history
+    st.session_state["chat_history"].append(("You", input)) 
+    st.subheader("Here is your Response:")
+    for part in response:
+        st.write(part.text)
+        st.session_state["chat_history"].append(("Bot", part.text)) 
+        
+st.subheader("The Chat History is: ")
+
+for role, text in st.session_state["chat_history"]:
+    st.write(f"{role}:{text}")
+    
+
+         

requirements.txt

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+streamlit
+google-generativeai
+python-dotenv