Update app.py

app.py

@@ -4,6 +4,7 @@ import os
 import json
 import pandas as pd
 import matplotlib.pyplot as plt
+import numpy as np
 
 # Function to call the Together API with the provided model
 def call_ai_model(all_message):
@@ -38,22 +39,42 @@ def call_ai_model(all_message):
 st.title("Impact of Climate on Sports Using AI")
 st.write("Predict and mitigate the impacts of climate change on sports performance and infrastructure.")
 
-# Input fields for user to enter data
+# Climate data inputs
 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)
 uv_index = st.number_input("UV Index:", min_value=0, max_value=11, value=5)
+air_quality_index = st.number_input("Air Quality Index:", min_value=0, max_value=500, value=100)
+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)
+
+# 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"])
+performance_history = st.text_area("Athlete Performance History:")
+
+# Infrastructure characteristics
+facility_type = st.selectbox("Facility Type:", ["Stadium", "Gymnasium", "Outdoor Field"])
+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"Predict the impact on sports performance and infrastructure given the following climate conditions: "
-        f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, UV Index {uv_index}."
+        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"Assess the impact on sports performance, infrastructure, and socio-economic aspects."
     )
-    
+
     try:
         with st.spinner("Generating response..."):
             response = call_ai_model(all_message)
-            
+
             generated_text = ""
             for line in response.iter_lines():
                 if line:
@@ -68,34 +89,30 @@ if st.button("Generate Prediction"):
                                 generated_text += delta["content"]
                     except json.JSONDecodeError:
                         continue
-            
-            # Display concise response and conclusion
-            st.success("Response generated!")
 
-            # Constructing the summary and conclusion
-            summary = f"**Impact Summary:** {generated_text.strip()}\n"
-            conclusion = "**Conclusion:** Proper adaptation to these climate conditions is essential for maintaining sports performance and infrastructure resilience."
+            st.success("Response generated!")
 
-            # Display text
-            st.markdown(summary)
-            st.markdown(conclusion)
+            # 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.")
 
-            # Example data for charts
+            # Data Visualization
+            st.subheader("Climate Condition Impacts Visualization")
+            # Example: Displaying data in a table
             data = {
-                'Condition': ['Temperature', 'Humidity', 'Wind Speed', 'UV Index'],
-                'Value': [temperature, humidity, wind_speed, uv_index]
+                '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)
-
-            # Displaying a table
             st.table(df)
 
-            # Plotting a bar chart
+            # Plotting a bar chart for climate variables
             fig, ax = plt.subplots()
-            ax.bar(data['Condition'], data['Value'], color=['blue', 'green', 'orange', 'red'])
+            ax.bar(data['Condition'], data['Value'], color=['blue', 'green', 'orange', 'red', 'purple', 'gray', 'cyan'])
             ax.set_ylabel('Value')
-            ax.set_title('Climate Condition Impact Indicators')
+            ax.set_title('Climate Condition Impacts')
             st.pyplot(fig)
+
     except ValueError as ve:
         st.error(f"Configuration error: {ve}")
     except requests.exceptions.RequestException as re: