Create app.py

app.py

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+import os
+import streamlit as st # type: ignore
+import google.generativeai as gen_ai # type: ignore
+from dotenv import load_dotenv
+# load environment variables
+load_dotenv()
+
+# Configure Streamlit page setting
+st.set_page_config(
+    page_title="Chat with GeminiPro", 
+    page_icon=":brain",
+    layout="centered")
+
+
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+#Setup Google GeminiPro AI Model
+gen_ai.configure(api_key=GOOGLE_API_KEY)
+model = gen_ai.GenerativeModel('gemini-pro')
+
+
+# Function to translate roles between GeminiPro and Streamlit terminology
+def translate_role_fo_streamlit(user_role):
+    if user_role == 'model':
+        return "Assistant"
+    else:
+        return user_role
+    
+
+# Initialize chat session in Streamlit if not already present
+if "chat_session" not in st.session_state:
+    st.session_state.chat_session = model.start_chat(history=[])
+
+
+# Display the Chatbot's title on the page
+st.title("🤖Gemini-Pro AI Chatbot")
+
+
+# Display the chat history
+for message in st.session_state.chat_session.history:
+    with st.chat_message(translate_role_fo_streamlit(message.role)):
+        st.markdown(message.parts[0].text) 
+
+
+# Input field for user's message
+user_prompt = st.chat_input("Ask GeminiPro")
+if user_prompt:
+    # Add users's message to chat and display it
+    st.chat_message("user").markdown(user_prompt)
+
+
+    # Send user's message to GeminiPro and get the respone
+    gemini_response = st.session_state.chat_session.send_message(user_prompt)
+
+
+    # Display GeminiPro's response
+    with st.chat_message("Assistant"):
+        st.markdown(gemini_response.text)