Update app.py

app.py

@@ -5,6 +5,8 @@ import streamlit as st
 import hmac
 import config
 from openai import OpenAI
+import matplotlib.pyplot as plt
+import numpy as np
 
 ############################################################################################################
 # Password protection
@@ -20,137 +22,131 @@ def check_password():
         else:
             st.session_state["password_correct"] = False
 
-    # Return True if the password is validated.
     if st.session_state.get("password_correct", False):
         return True
 
-    # Show input for password.
-    st.text_input(
-        "Password", type="password", on_change=password_entered, key="password"
-    )
+    st.text_input("Password", type="password", on_change=password_entered, key="password")
     if "password_correct" in st.session_state:
         st.error("😕 Password incorrect")
     return False
 
 if not check_password():
-    st.stop()  # Do not continue if check_password is not True.
+    st.stop()
 
 ############################################################################################################
 # Streamlit app layout
 
-# Set the page to wide or centered mode
-st.set_page_config(layout="wide", 
-                   page_title="Modular Chatbot",
-                   page_icon=":lightbulb:",
-                   initial_sidebar_state="collapsed"
-                   )
+st.set_page_config(
+    layout="wide", 
+    page_title="Modular Chatbot + Growth Model",
+    page_icon=":chart_with_upwards_trend:",
+    initial_sidebar_state="expanded"
+)
 
-# Streamlit app layout
-# st.title(config.app_title)
-# with st.expander("INSTRUCTIONS FOR STUDENTS:"):
-#     st.markdown(config.instructions)
-
-############################################################################################################
-
-
-# Define a basic initial context at the beginning of your script
+# Initial context for chatbot
 initial_context = {
     "role": "system",
     "content": config.prompt
 }
 
-# Initialize the OpenAI client
 client = OpenAI(api_key=st.secrets["OPENAI_API_KEY"])
 
-# Initialize the session state variables if they don't exist
 if "openai_model" not in st.session_state:
     st.session_state["openai_model"] = config.ai_model
 
-# Corrected initialization of display_messages:
 if "display_messages" not in st.session_state:
-    st.session_state["display_messages"] = [initial_context] 
-    
-# Initialize chat with opening message if it doesn't exist
+    st.session_state["display_messages"] = [initial_context]
+
 if "chat_initialized" not in st.session_state:
     st.session_state["chat_initialized"] = True
-    # Add the opening message to the display
     st.session_state["display_messages"].append({"role": "assistant", "content": config.opening_message})
 
-# Get user input
 prompt = st.chat_input("Type your message here...")
 
-# Input for new messages
 if prompt:
-    # Ensure initial context is in the session state and then append user messages
     if not st.session_state["display_messages"]:
         st.session_state["display_messages"] = [initial_context]
     st.session_state["display_messages"].append({"role": "user", "content": prompt})
 
-# Function to reset all chat history
 def reset_chat_history():
     st.session_state["display_messages"] = [initial_context]
-    # Re-add opening message when chat is reset
     st.session_state["display_messages"].append({"role": "assistant", "content": config.opening_message})
     st.rerun()
 
-# Main chat container
+# Main chat display
 with st.container(border=False):
-    # Display chat history in reverse order including new messages
     for message in st.session_state["display_messages"][1:]:
-        if message["role"] == "user":
-            with st.chat_message("user"):
-                st.markdown(message["content"])
-        else:
-            with st.chat_message("assistant"):
-                st.markdown(message["content"])
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
 
-# Generate assistant's response and add it to the messages
     if prompt:
         with st.chat_message("assistant"):
             try:
                 stream = client.chat.completions.create(
                     model=st.session_state["openai_model"],
-                    messages=[
-                        {"role": m["role"], "content": m["content"]}
-                        for m in st.session_state["display_messages"]
-                    ],
+                    messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state["display_messages"]],
                     stream=True,
                     temperature=config.temperature,
                     max_tokens=config.max_tokens,
                     frequency_penalty=config.frequency_penalty,
                     presence_penalty=config.presence_penalty,
                 )
-                
-                # Initialize an empty string to store the full response
                 full_response = ""
                 message_placeholder = st.empty()
-                
-                # Iterate through the stream to get each chunk
                 for chunk in stream:
                     if chunk.choices[0].delta.content is not None:
                         full_response += chunk.choices[0].delta.content
                         message_placeholder.markdown(full_response + "▌")
-                
-                # Replace the placeholder with the complete message
                 message_placeholder.markdown(full_response)
-                
-                # Append the full response to the session state for display
-                st.session_state["display_messages"].append(
-                    {"role": "assistant", "content": full_response}
-                )
+                st.session_state["display_messages"].append({"role": "assistant", "content": full_response})
             except Exception as e:
                 st.error(f"An error occurred: {str(e)}")
 
-# Create a sidebar
+############################################################################################################
+# Sidebar and Growth Model Controls
+
 with st.sidebar:
     st.markdown(config.warning_message, unsafe_allow_html=True)
 
-    # Add Clear Chat History button to sidebar
     if st.button("Clear Chat History"):
         reset_chat_history()
-    
-    # Add license link with markdown
-    st.markdown("---")  # Separator line
-    st.markdown("""
-        <small>Licensed under [GNU GPL v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html)</small>
-        """, unsafe_allow_html=True)
+
+    st.markdown("---")
+    st.markdown("<small>Licensed under [GNU GPL v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html)</small>", unsafe_allow_html=True)
+
+    st.markdown("## 📈 Population Growth Model")
+    with st.form("growth_model_form"):
+        model_type = st.radio("Model type:", ["Exponential", "Logistic"])
+        N0 = st.number_input("Initial population size (N₀):", min_value=0.0, value=10.0)
+        r = st.number_input("Growth rate (r):", value=0.1)
+        K = None
+        if model_type == "Logistic":
+            K = st.number_input("Carrying capacity (K):", min_value=1.0, value=100.0)
+        t_max = st.number_input("Time span (t max):", min_value=1.0, value=50.0)
+        submit_model = st.form_submit_button("Generate Growth Plot")
+
+############################################################################################################
+# Growth Model Plot
+
+if submit_model:
+    try:
+        t = np.linspace(0, t_max, 500)
+        if model_type == "Exponential":
+            N = N0 * np.exp(r * t)
+        elif model_type == "Logistic":
+            N = K / (1 + ((K - N0) / N0) * np.exp(-r * t))
+        else:
+            raise ValueError("Invalid model type selected.")
+
+        st.subheader("📊 Population Growth Plot")
+        fig, ax = plt.subplots()
+        ax.plot(t, N, label=model_type, color="green" if model_type == "Logistic" else "blue")
+        ax.set_xlabel("Time")
+        ax.set_ylabel("Population Size")
+        ax.set_title(f"{model_type} Growth Model")
+        ax.grid(True)
+        ax.legend()
+        st.pyplot(fig)
+
+    except Exception as e:
+        st.error(f"Error generating plot: {e}")