Update app.py

app.py

@@ -35,6 +35,35 @@ def call_ai_model(all_message):
 
     return response
 
+# Function to generate performance data
+def generate_performance_data(temperature_range):
+    performance_data = []
+    for temp in temperature_range:
+        message = (
+            f"Generate performance data for a sport under the following condition: "
+            f"Temperature {temp}°C. Provide a single value for performance."
+        )
+        response = call_ai_model(message)
+
+        performance_value = ""
+        for line in response.iter_lines():
+            if line:
+                line_content = line.decode('utf-8')
+                if line_content.startswith("data: "):
+                    line_content = line_content[6:]  # Strip "data: " prefix
+                try:
+                    json_data = json.loads(line_content)
+                    if "choices" in json_data:
+                        delta = json_data["choices"][0]["delta"]
+                        if "content" in delta:
+                            performance_value += delta["content"]
+                except json.JSONDecodeError:
+                    continue
+        
+        performance_data.append(float(performance_value.strip()))
+
+    return performance_data
+
 # Streamlit app layout
 st.title("Climate Impact on Sports Performance and Infrastructure")
 st.write("Analyze and visualize the impact of climate conditions on sports performance and infrastructure.")
@@ -58,7 +87,7 @@ if st.button("Generate Prediction"):
         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"After analyzing that I want you to visualize the data in the best way possible, might be in a table, using a chart or any other way so that it could be easy to understand"
+        f"After analyzing that, visualize the data in the best way possible, might be in a table, using a chart or any other way so that it could be easy to understand"
     )
 
     try:
@@ -139,16 +168,22 @@ if st.button("Generate Prediction"):
             st.markdown("**Predicted Impact on Performance and Infrastructure:**")
             st.markdown(generated_text.strip())
 
-            # Select conditions to visualize
-            conditions = ["Humidity", "Wind Speed", "UV Index", "Air Quality Index", "Precipitation", "Atmospheric Pressure"]
-            selected_conditions = st.multiselect("Select conditions to visualize against Temperature:", conditions, default=conditions)
+            # Generate performance data for different temperatures
+            temp_range = [temperature - 5, temperature, temperature + 5]
+            performance_values = generate_performance_data(temp_range)
+
+            # Create a dataframe for performance values
+            performance_df = pd.DataFrame({
+                "Temperature": temp_range,
+                "Performance": performance_values
+            })
 
-            # Generate a line chart to show the relationship between temperature and selected conditions
+            # Generate a line chart to show the relationship between temperature and performance
             fig, ax = plt.subplots()
-            for condition in selected_conditions:
-                ax.plot(["Temperature", condition], [temperature, results_data["Value"][results_data["Condition"].index(condition)]], marker='o', label=condition)
-            ax.set_ylabel('Values')
-            ax.set_title('Relationship Between Temperature and Selected Conditions')
+            ax.plot(performance_df["Temperature"], performance_df["Performance"], marker='o', label="Performance")
+            ax.set_xlabel('Temperature (°C)')
+            ax.set_ylabel('Performance')
+            ax.set_title('Relationship Between Temperature and Sports Performance')
             ax.legend()
             st.pyplot(fig)