Update app.py

app.py

@@ -36,7 +36,6 @@ def call_ai_model(all_message):
 
 # Function to get performance data from AI
 def get_performance_data(conditions):
-    url = "https://api.together.xyz/v1/chat/completions"
     all_message = (
         f"Provide the expected sports performance score at conditions: "
         f"Temperature {conditions['temperature']}°C, Humidity {conditions['humidity']}%, "
@@ -60,7 +59,9 @@ def get_performance_data(conditions):
             except json.JSONDecodeError:
                 continue
 
-    return generated_text.strip()
+    # 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
 
 # Streamlit app layout
 st.title("Climate Impact on Sports Performance")
@@ -89,7 +90,7 @@ if st.button("Generate Prediction"):
 
     try:
         with st.spinner("Generating predictions..."):
-            # Call AI model to get initial prediction and qualitative assessment
+            # Call AI model to get qualitative analysis
             qualitative_analysis = (
                 f"Assess the impact on sports performance at conditions: "
                 f"Temperature {temperature}°C, Humidity {humidity}%, "
@@ -100,7 +101,7 @@ if st.button("Generate Prediction"):
             qualitative_result = call_ai_model(qualitative_analysis)
 
             # Get performance score for specified conditions
-            performance_score_text = get_performance_data(conditions)
+            performance_scores = get_performance_data(conditions)
 
         st.success("Predictions generated.")
 
@@ -110,33 +111,22 @@ if st.button("Generate Prediction"):
 
         # Display performance score
         st.subheader("Performance Score")
-        st.write(f"Predicted Performance Score: {performance_score_text}")
+        st.write(f"Predicted Performance Scores: {performance_scores}")
 
         # Plotting the data
         st.subheader("Performance Score vs Climate Conditions")
 
-        # Define some dummy data for the climate conditions
+        # Define climate conditions for plotting
         climate_conditions = list(conditions.keys())
         climate_values = list(conditions.values())
 
-        # Prepare data for plotting (replace with actual performance scores from API)
-        performance_scores = [80, 75, 85, 70, 78, 82, 72]  # Replace with actual data from API
-
-        fig, ax1 = plt.subplots()
-
-        # Plot climate conditions on the primary y-axis
-        ax1.plot(climate_conditions, climate_values, marker='o', color='b', label='Climate Conditions')
-        ax1.set_xlabel('Climate Conditions')
-        ax1.set_ylabel('Values', color='b')
-        ax1.tick_params(axis='y', labelcolor='b')
-
-        # Create a secondary y-axis for performance score
-        ax2 = ax1.twinx()
-        ax2.plot(['Performance Score'], performance_scores, marker='s', color='r', label='Performance Score')
-        ax2.set_ylabel('Performance Score', color='r')
-        ax2.tick_params(axis='y', labelcolor='r')
-
-        fig.tight_layout()
+        # Plotting performance score against climate conditions
+        fig, ax = plt.subplots()
+        ax.plot(climate_conditions, performance_scores, marker='o', linestyle='-', color='b')
+        ax.set_xlabel('Climate Conditions')
+        ax.set_ylabel('Performance Score')
+        ax.set_title('Performance Score vs Climate Conditions')
+        ax.grid(True)
         st.pyplot(fig)
 
     except ValueError as ve: