Update app.py

app.py

@@ -3,6 +3,7 @@ import requests
 import os
 import json
 import pandas as pd
+import plotly.graph_objects as go
 
 # Function to call the Together AI model for the initial analysis
 def call_ai_model_initial(all_message):
@@ -75,17 +76,23 @@ air_quality_index = st.number_input("Air Quality Index:", min_value=0, max_value
 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)
 
-# Geographic location input
-latitude = st.number_input("Latitude:", min_value=-90.0, max_value=90.0, value=0.0)
-longitude = st.number_input("Longitude:", min_value=-180.0, max_value=180.0, value=0.0)
+# Sports and athlete inputs
+sports = st.multiselect("Select sports:", ["Football", "Tennis", "Athletics", "Swimming", "Basketball", "Golf"])
+athlete_types = st.multiselect("Select athlete types:", ["Professional", "Amateur", "Youth", "Senior"])
+
+# Infrastructure inputs
+infrastructure_types = st.multiselect("Select infrastructure types:", ["Outdoor Stadium", "Indoor Arena", "Swimming Pool", "Tennis Court", "Golf Course"])
 
 if st.button("Generate Prediction"):
     all_message = (
-        f"Assess the impact on sports performance and infrastructure based on climate conditions: "
+        f"Assess the impact on sports performance, athletes, and infrastructure based on climate conditions: "
         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, should be in a table, so that it could be easy to understand."
+        f"Sports: {', '.join(sports)}. Athlete types: {', '.join(athlete_types)}. "
+        f"Infrastructure types: {', '.join(infrastructure_types)}. "
+        f"Provide a detailed analysis of how these conditions affect performance, health, and infrastructure. "
+        f"Include specific impacts for each sport, athlete type, and infrastructure type. "
+        f"Also, provide an overall performance score and an infrastructure impact score, both as percentages."
     )
 
     try:
@@ -111,8 +118,9 @@ if st.button("Generate Prediction"):
 
         with st.spinner("Generating predictions..."):
             analysis_text = (
-                f"Analyze the following text and only give me one short sentense performance score (as a percentage) based on the climate conditions and their impact. "
-                f"Please include a line that says 'Performance Score: XX%' in your response. Here's the text to analyze: {initial_text}"
+                f"Based on the following analysis, provide a performance score and an infrastructure impact score, "
+                f"both as percentages. Include lines that say 'Performance Score: XX%' and 'Infrastructure Impact Score: YY%' "
+                f"in your response. Here's the text to analyze: {initial_text}"
             )
             analysis_response = call_ai_model_analysis(analysis_text)
 
@@ -133,12 +141,14 @@ if st.button("Generate Prediction"):
 
             st.success("Predictions generated!")
 
-            # Extract performance score from the analysis result
+            # Extract performance and infrastructure scores from the analysis result
             performance_score = "N/A"
+            infrastructure_score = "N/A"
             for line in analysis_result.split('\n'):
-                if "performance" in line.lower() and "score" in line.lower():
+                if "performance score:" in line.lower():
                     performance_score = line.split(":")[-1].strip()
-                    break
+                elif "infrastructure impact score:" in line.lower():
+                    infrastructure_score = line.split(":")[-1].strip()
 
             # Prepare data for visualization
             results_data = {
@@ -148,19 +158,53 @@ if st.button("Generate Prediction"):
             results_df = pd.DataFrame(results_data)
 
             # Display results in a table
-            st.subheader("Results Summary")
+            st.subheader("Climate Conditions Summary")
             st.table(results_df)
 
+            # Create a radar chart for climate conditions
+            fig = go.Figure(data=go.Scatterpolar(
+                r=[temperature/50*100, humidity, wind_speed/2, uv_index/11*100, air_quality_index/5, precipitation/5, (atmospheric_pressure-900)/2],
+                theta=results_df['Condition'],
+                fill='toself'
+            ))
+            fig.update_layout(
+                polar=dict(
+                    radialaxis=dict(visible=True, range=[0, 100])
+                ),
+                showlegend=False
+            )
+            st.plotly_chart(fig)
+
             # Display prediction
-            st.markdown("**Predicted Impact on Performance and Infrastructure:**")
+            st.subheader("Predicted Impact on Performance and Infrastructure")
             st.markdown(initial_text.strip())
 
-            # Display performance score
-            st.markdown(f"**Performance Score:** {performance_score}")
+            # Display performance and infrastructure scores
+            col1, col2 = st.columns(2)
+            with col1:
+                st.metric("Performance Score", performance_score)
+            with col2:
+                st.metric("Infrastructure Impact Score", infrastructure_score)
+
+            # Display analyzed sports and infrastructure
+            st.subheader("Analyzed Components")
+            col1, col2, col3 = st.columns(3)
+            with col1:
+                st.write("**Sports:**")
+                for sport in sports:
+                    st.write(f"- {sport}")
+            with col2:
+                st.write("**Athlete Types:**")
+                for athlete_type in athlete_types:
+                    st.write(f"- {athlete_type}")
+            with col3:
+                st.write("**Infrastructure Types:**")
+                for infra_type in infrastructure_types:
+                    st.write(f"- {infra_type}")
 
             # Display raw analysis result for debugging
-            st.subheader("Raw Analysis Result")
-            st.text(analysis_result)
+            with st.expander("Show Raw Analysis"):
+                st.text(analysis_result)
 
     except ValueError as ve:
         st.error(f"Configuration error: {ve}")