Update app.py

app.py

@@ -3,8 +3,8 @@ import requests
 import os
 import json
 import pandas as pd
-import matplotlib.pyplot as plt
-from PIL import Image
+import folium  # For creating the map visualizations
+from folium.plugins import MarkerCluster
 
 # Function to call the Together API with the provided model
 def call_ai_model(all_message):
@@ -48,6 +48,10 @@ air_quality_index = st.number_input("Air Quality Index:", min_value=0, max_value
 precipitation = st.number_input("Precipitation (mm):", min_value=0.0, max_value=500.0, value=10.0)
 atmospheric_pressure = st.number_input("Atmospheric Pressure (hPa):", min_value=900, max_value=1100, value=1013)
 
+# Geographic location
+latitude = st.number_input("Latitude:", min_value=-90.0, max_value=90.0, value=0.0)
+longitude = st.number_input("Longitude:", min_value=-180.0, max_value=180.0, value=0.0)
+
 # Athlete-specific inputs
 age = st.number_input("Athlete Age:", min_value=0, max_value=100, value=25)
 sport = st.selectbox("Select Sport:", ["Running", "Cycling", "Swimming", "Football", "Basketball"])
@@ -66,8 +70,8 @@ if st.button("Generate Prediction"):
     all_message = (
         f"Given the climate conditions: Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, "
         f"UV Index {uv_index}, Air Quality Index {air_quality_index}, Precipitation {precipitation} mm, "
-        f"Atmospheric Pressure {atmospheric_pressure} hPa. For athlete (Age: {age}, Sport: {sport}), "
-        f"Facility (Type: {facility_type}, Age: {facility_age}, Materials: {materials_used}). "
+        f"Atmospheric Pressure {atmospheric_pressure} hPa. Location: Latitude {latitude}, Longitude {longitude}. "
+        f"For athlete (Age: {age}, Sport: {sport}), Facility (Type: {facility_type}, Age: {facility_age}, Materials: {materials_used}). "
         f"Assess the impact on sports performance, infrastructure, and socio-economic aspects."
     )
 
@@ -98,17 +102,20 @@ if st.button("Generate Prediction"):
 
             # Displaying a table of input data
             data = {
-                'Condition': ['Temperature', 'Humidity', 'Wind Speed', 'UV Index', 'Air Quality Index', 'Precipitation', 'Atmospheric Pressure'],
-                'Value': [temperature, humidity, wind_speed, uv_index, air_quality_index, precipitation, atmospheric_pressure]
+                'Condition': ['Temperature', 'Humidity', 'Wind Speed', 'UV Index', 'Air Quality Index', 'Precipitation', 'Atmospheric Pressure', 'Latitude', 'Longitude'],
+                'Value': [temperature, humidity, wind_speed, uv_index, air_quality_index, precipitation, atmospheric_pressure, latitude, longitude]
             }
             df = pd.DataFrame(data)
             st.subheader("Input Data Overview")
             st.table(df)
 
-            # Display an infographic
-            infographics_path = 'path_to_your_infographic_image.jpg'  # Replace with your image path
-            infographic = Image.open(infographics_path)
-            st.image(infographic, caption='Climate Impact Infographic', use_column_width=True)
+            # Creating a map with the provided location
+            map_center = [latitude, longitude]
+            sport_map = folium.Map(location=map_center, zoom_start=12)
+            marker_cluster = MarkerCluster().add_to(sport_map)
+            folium.Marker(location=map_center, popup="User Location").add_to(marker_cluster)
+            map_html = sport_map._repr_html_()
+            st.components.v1.html(map_html, height=600)
 
     except ValueError as ve:
         st.error(f"Configuration error: {ve}")