Update app.py

app.py

@@ -38,10 +38,7 @@ 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, Humidity {conditions['humidity']}%, "
-        f"Wind Speed {conditions['wind_speed']} km/h, UV Index {conditions['uv_index']}, "
-        f"Air Quality Index {conditions['air_quality_index']}, Precipitation {conditions['precipitation']} mm, "
-        f"Atmospheric Pressure {conditions['atmospheric_pressure']} hPa."
+        f"Temperature {conditions['temperature']}°C."
     )
     response = call_ai_model(all_message)
     generated_text = ""
@@ -65,38 +62,23 @@ def get_performance_data(conditions):
 
 # Streamlit app layout
 st.title("Climate Impact on Sports Performance")
-st.write("Analyze and visualize the impact of climate conditions on sports performance.")
+st.write("Analyze and visualize the impact of temperature on sports performance.")
 
-# Inputs for climate conditions
+# Input for temperature
 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)
 
 # Button to generate predictions
 if st.button("Generate Prediction"):
     conditions = {
-        "temperature": temperature,
-        "humidity": humidity,
-        "wind_speed": wind_speed,
-        "uv_index": uv_index,
-        "air_quality_index": air_quality_index,
-        "precipitation": precipitation,
-        "atmospheric_pressure": atmospheric_pressure
+        "temperature": temperature
     }
 
     try:
         with st.spinner("Generating predictions..."):
             # Call AI model to get qualitative analysis
             qualitative_analysis = (
-                f"Assess the impact on sports performance at conditions: "
-                f"Temperature {temperature}°C, Humidity {humidity}%, "
-                f"Wind Speed {wind_speed} km/h, UV Index {uv_index}, "
-                f"Air Quality Index {air_quality_index}, Precipitation {precipitation} mm, "
-                f"Atmospheric Pressure {atmospheric_pressure} hPa."
+                f"Assess the impact on sports performance at temperature: "
+                f"{temperature}°C."
             )
             qualitative_result = call_ai_model(qualitative_analysis)
 
@@ -114,18 +96,14 @@ if st.button("Generate Prediction"):
         st.write(f"Predicted Performance Scores: {performance_scores}")
 
         # Plotting the data
-        st.subheader("Performance Score vs Climate Conditions")
+        st.subheader("Performance Score vs Temperature")
 
-        # Define climate conditions for plotting
-        climate_conditions = list(conditions.keys())
-        climate_values = list(conditions.values())
-
-        # Plotting performance score against climate conditions
+        # Plot performance score against temperature
         fig, ax = plt.subplots()
-        ax.plot(climate_conditions, performance_scores, marker='o', linestyle='-', color='b')
-        ax.set_xlabel('Climate Conditions')
+        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 Climate Conditions')
+        ax.set_title('Performance Score vs Temperature')
         ax.grid(True)
         st.pyplot(fig)