Update app.py

app.py

@@ -3,10 +3,10 @@ import requests
 import os
 import json
 import pandas as pd
-import folium  # For creating the map visualizations
+import folium  # For map visualizations
 from folium.plugins import MarkerCluster
 
-# Function to call the Together API with the provided model
+# Function to call the Together AI model
 def call_ai_model(all_message):
     url = "https://api.together.xyz/v1/chat/completions"
     payload = {
@@ -36,10 +36,10 @@ def call_ai_model(all_message):
     return response
 
 # Streamlit app layout
-st.title("Impact of Climate on Sports Using AI")
-st.write("Predict and mitigate the impacts of climate change on sports performance and infrastructure.")
+st.title("Climate Impact on Sports Performance and Infrastructure")
+st.write("Analyze and visualize the impact of climate conditions on sports performance and infrastructure.")
 
-# Climate data inputs
+# Inputs for climate conditions
 temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25)
 humidity = st.number_input("Humidity (%):", min_value=0, max_value=100, value=50)
 wind_speed = st.number_input("Wind Speed (km/h):", min_value=0.0, max_value=200.0, value=15.0)
@@ -52,7 +52,7 @@ atmospheric_pressure = st.number_input("Atmospheric Pressure (hPa):", min_value=
 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
+# Athlete-specific data
 age = st.number_input("Athlete Age:", min_value=0, max_value=100, value=25)
 sport = st.selectbox("Select Sport:", ["Running", "Cycling", "Swimming", "Football", "Basketball"])
 performance_history = st.text_area("Athlete Performance History:")
@@ -62,17 +62,13 @@ facility_type = st.selectbox("Facility Type:", ["Stadium", "Gymnasium", "Outdoor
 facility_age = st.number_input("Facility Age (years):", min_value=0, max_value=100, value=10)
 materials_used = st.text_input("Materials Used in Construction:")
 
-# Socio-economic data
-community_size = st.number_input("Community Size:", min_value=0, value=1000)
-economic_impact_estimate = st.text_area("Estimate Economic Impact (Event cancellations, Facility damage costs):")
-
 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. 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."
+        f"Assess the impact on sports performance and infrastructure based on climate conditions: "
+        f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, UV Index {uv_index}, "
+        f"Air Quality Index {air_quality_index}, Precipitation {precipitation} mm, Atmospheric Pressure {atmospheric_pressure} hPa. "
+        f"Location: Latitude {latitude}, Longitude {longitude}. "
+        f"Athlete (Age: {age}, Sport: {sport}), Facility (Type: {facility_type}, Age: {facility_age}, Materials: {materials_used})."
     )
 
     try:
@@ -96,26 +92,30 @@ if st.button("Generate Prediction"):
 
             st.success("Response generated!")
 
-            # Display the impact summary and conclusions
-            st.markdown(f"**Impact Summary:** {generated_text.strip()}")
-            st.markdown("**Conclusion:** Tailoring strategies based on these climate conditions can significantly enhance performance and infrastructure resilience.")
-
-            # Displaying a table of input data
-            data = {
-                '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]
+            # Prepare data for visualization
+            results_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]
             }
-            df = pd.DataFrame(data)
-            st.subheader("Input Data Overview")
-            st.table(df)
+            results_df = pd.DataFrame(results_data)
+
+            # Display results in a table
+            st.subheader("Results Summary")
+            st.table(results_df)
+
+            # Display prediction
+            st.markdown("**Predicted Impact on Performance and Infrastructure:**")
+            st.markdown(generated_text.strip())
 
-            # Creating a map with the provided location
+            # Create a map with the user's 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)
+            MarkerCluster().add_to(sport_map).add_child(
+                folium.Marker(location=map_center, popup="User Location")
+            )
+            st.subheader("Geographical Visualization")
+            st.write("Map showing the location's impact on sports performance and infrastructure.")
+            st.components.v1.html(sport_map._repr_html_(), height=600)
 
     except ValueError as ve:
         st.error(f"Configuration error: {ve}")