vcncnnUpdate Chatbot.py

Chatbot.py

@@ -1,29 +1,49 @@
-from openai import OpenAI
-import streamlit as st
-
-with st.sidebar:
-    openai_api_key = st.text_input("OpenAI API Key", key="chatbot_api_key", type="password")
-    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
-    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/Chatbot.py)"
-    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
-
-st.title("💬 Chatbot")
-st.caption("🚀 A Streamlit chatbot powered by OpenAI")
-if "messages" not in st.session_state:
-    st.session_state["messages"] = [{"role": "assistant", "content": "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():
-    if not openai_api_key:
-        st.info("Please add your OpenAI API key to continue.")
-        st.stop()
-
-    client = OpenAI(api_key=openai_api_key)
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(prompt)
-    response = client.chat.completions.create(model="gpt-3.5-turbo", messages=st.session_state.messages)
-    msg = response.choices[0].message.content
-    st.session_state.messages.append({"role": "assistant", "content": msg})
-    st.chat_message("assistant").write(msg)
+# Simulate a geospatial heatmap and predictive simulation for the enhanced dashboard
+
+import geopandas as gpd
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+
+# Mock data for regions with adherence rates and infection projections
+regions = {
+    "Region A": {"adherence": 85, "projected_infection": 5, "lat": 0.1, "lon": 36.8},
+    "Region B": {"adherence": 78, "projected_infection": 10, "lat": 1.3, "lon": 36.9},
+    "Region C": {"adherence": 65, "projected_infection": 25, "lat": -1.5, "lon": 37.1},
+    "Region D": {"adherence": 90, "projected_infection": 2, "lat": 0.8, "lon": 37.3},
+}
+
+# Convert mock data into a GeoDataFrame
+data = {
+    "Region": list(regions.keys()),
+    "Adherence": [regions[reg]["adherence"] for reg in regions],
+    "Projected_Infection": [regions[reg]["projected_infection"] for reg in regions],
+    "Latitude": [regions[reg]["lat"] for reg in regions],
+    "Longitude": [regions[reg]["lon"] for reg in regions],
+}
+gdf = gpd.GeoDataFrame(data, geometry=gpd.points_from_xy(data["Longitude"], data["Latitude"]))
+
+# Set up map plotting with adherence heatmap
+fig, ax = plt.subplots(1, 2, figsize=(16, 8))
+
+# Heatmap for adherence rates
+cmap = ListedColormap(["red", "orange", "green"])
+categories = [65, 75, 100]  # Categories for adherence
+adherence_colors = ["red" if x < 70 else "orange" if x < 80 else "green" for x in gdf["Adherence"]]
+
+gdf.plot(ax=ax[0], color=adherence_colors, markersize=100, edgecolor="black")
+ax[0].set_title("Geospatial Adherence Rates", fontsize=14)
+for x, y, label in zip(gdf["Longitude"], gdf["Latitude"], gdf["Region"]):
+    ax[0].text(x + 0.05, y, label, fontsize=10)
+
+# Predicted Infection Simulation Bar
+ax[1].bar(gdf["Region"], gdf["Projected_Infection"], color="blue")
+ax[1].set_title("Predicted Infection Rate Increase (6 Months)", fontsize=14)
+ax[1].set_ylabel("Predicted Increase (%)", fontsize=12)
+for i, val in enumerate(gdf["Projected_Infection"]):
+    ax[1].text(i, val + 1, f"+{val}%", ha="center", fontsize=10)
+
+# Overall Layout
+plt.suptitle("ImpactLens AI: Enhanced Geospatial & Predictive Insights", fontsize=16, weight="bold")
+plt.tight_layout(rect=[0, 0, 1, 0.95])
+
+plt.show()