Create app.py

app.py

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+import streamlit as st
+from google import genai
+from google.genai import types
+from openai import OpenAI
+
+# Show title and description.
+st.title("💬 LSAT Tutor")
+st.write(
+    "Hey there! I'm your tutor for today. We'll revise the LSAT Logical Reasoning Section."
+)
+
+# Ask user for their OpenAI API key via `st.text_input`.
+# Alternatively, you can store the API key in `./.streamlit/secrets.toml` and access it
+# via `st.secrets`, see https://docs.streamlit.io/develop/concepts/connections/secrets-management
+# openai_api_key = st.text_input("OpenAI API Key", type="password")
+GEMINI_API_KEY = "AIzaSyAjpHA08BUwLhK-tIlORxcB18RAp3541-M"
+
+# Create a client.
+client = genai.Client(api_key=GEMINI_API_KEY)
+
+# Create a session state variable to store the chat messages. This ensures that the
+# messages persist across reruns.
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display the existing chat messages via `st.chat_message`.
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Create a chat input field to allow the user to enter a message. This will display
+# automatically at the bottom of the page.
+if prompt := st.chat_input("Ready to begin?"):
+
+    # Store and display the current prompt.
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Generate a response using the OpenAI API.
+      #   stream = client.chat.completions.create(
+      #   model="gemini-2.0-flash",
+      #   # config=types.GenerateContentConfig(
+      #   # system_instruction=system_instruction,
+      #   # tools=[tools]),
+      #   messages=[
+      #       {"role": m["role"], "content": m["content"]}
+      #       for m in st.session_state.messages
+      #   ],
+      #   stream=True,
+      # )
+
+    stream = client.chats.create(model="gemini-2.0-flash",
+        # messages = [
+        #     {"role": m["role"], "content": m["content"]}
+        #     for m in st.session_state.messages
+        # ]
+    # config=types.GenerateContentConfig(
+    #   system_instruction=system_instruction,
+    #   tools=[tools]
+    # )
+    )
+
+        # Stream the response to the chat using `st.write_stream`, then store it in 
+    # session state.
+    with st.chat_message("assistant"):
+        response = st.write_stream(stream.send_message(prompt))
+    st.session_state.messages.append({"role": "assistant", "content": response})
+
+
+
+    
+    
+    # stream = client.chat.completions.create(
+    #     model="gpt-3.5-turbo",
+    #     messages=[
+    #         {"role": m["role"], "content": m["content"]}
+    #         for m in st.session_state.messages
+    #     ],
+    #     stream=True,
+    # )
+
+
+
+
+# import streamlit as st
+# import random
+# import time
+
+
+# # Streamed response emulator
+# def response_generator():
+#     response = random.choice(
+#         [
+#             "Hello there! How can I assist you today?",
+#             "Hi, human! Is there anything I can help you with?",
+#             "Hi there. Do you need help?",
+#         ]
+#     )
+#     for word in response.split():
+#         yield word + " "
+#         time.sleep(0.05)
+
+
+# st.title("Simple chat")
+
+# # Initialize chat history
+# if "messages" not in st.session_state:
+#     st.session_state.messages = []
+
+# # Display chat messages from history on app rerun
+# for message in st.session_state.messages:
+#     with st.chat_message(message["role"]):
+#         st.markdown(message["content"])
+
+# # Accept user input
+# if prompt := st.chat_input("What is up?"):
+#     # Add user message to chat history
+#     st.session_state.messages.append({"role": "user", "content": prompt})
+#     # Display user message in chat message container
+#     with st.chat_message("user"):
+#         st.markdown(prompt)
+
+#     # Display assistant response in chat message container
+#     with st.chat_message("assistant"):
+#         response = st.write_stream(response_generator())
+#     # Add assistant response to chat history
+#     st.session_state.messages.append({"role": "assistant", "content": response})