Update app.py

app.py

@@ -38,7 +38,8 @@ def call_ai_model(all_message):
 def get_performance_data(conditions):
     all_message = (
         f"Provide the expected sports performance score at conditions: "
-        f"Temperature {conditions['temperature']}°C."
+        f"Temperature {conditions['temperature']}°C, Humidity {conditions['humidity']}%, "
+        f"Wind Speed {conditions['wind_speed']} km/h."
     )
     response = call_ai_model(all_message)
     generated_text = ""
@@ -57,33 +58,38 @@ def get_performance_data(conditions):
                 continue
 
     # Example: Replace with actual data from API
-    performance_scores = [75, 80, 70, 85, 78, 72, 82]  # Replace with actual data from API
-    return performance_scores
+    performance_score = 80  # Replace with actual data from API
+    return performance_score
 
 # Streamlit app layout
 st.title("Climate Impact on Sports Performance")
-st.write("Analyze and visualize the impact of temperature on sports performance.")
+st.write("Analyze and visualize the impact of climate conditions on sports performance.")
 
-# Input for temperature
+# 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)
 
 # Button to generate predictions
 if st.button("Generate Prediction"):
     conditions = {
-        "temperature": temperature
+        "temperature": temperature,
+        "humidity": humidity,
+        "wind_speed": wind_speed
     }
 
     try:
         with st.spinner("Generating predictions..."):
             # Call AI model to get qualitative analysis
             qualitative_analysis = (
-                f"Assess the impact on sports performance at temperature: "
-                f"{temperature}°C."
+                f"Assess the impact on sports performance at conditions: "
+                f"Temperature {temperature}°C, Humidity {humidity}%, "
+                f"Wind Speed {wind_speed} km/h."
             )
             qualitative_result = call_ai_model(qualitative_analysis)
 
             # Get performance score for specified conditions
-            performance_scores = get_performance_data(conditions)
+            performance_score = get_performance_data(conditions)
 
         st.success("Predictions generated.")
 
@@ -93,17 +99,21 @@ if st.button("Generate Prediction"):
 
         # Display performance score
         st.subheader("Performance Score")
-        st.write(f"Predicted Performance Scores: {performance_scores}")
+        st.write(f"Predicted Performance Score: {performance_score}")
 
         # Plotting the data
-        st.subheader("Performance Score vs Temperature")
+        st.subheader("Performance Score vs Climate Conditions")
 
-        # Plot performance score against temperature
+        # Define climate conditions for plotting
+        climate_conditions = list(conditions.keys())
+        climate_values = list(conditions.values())
+
+        # Plotting performance score against climate conditions
         fig, ax = plt.subplots()
-        ax.plot(conditions['temperature'], performance_scores, marker='o', linestyle='-', color='b')
-        ax.set_xlabel('Temperature (°C)')
-        ax.set_ylabel('Performance Score')
-        ax.set_title('Performance Score vs Temperature')
+        ax.plot(climate_conditions, climate_values, marker='o', linestyle='-', color='b')
+        ax.set_xlabel('Climate Conditions')
+        ax.set_ylabel('Value')
+        ax.set_title('Performance Score vs Climate Conditions')
         ax.grid(True)
         st.pyplot(fig)