Update app.py

app.py

@@ -2,6 +2,8 @@ import streamlit as st
 import requests
 import os
 import json
+import pandas as pd
+import matplotlib.pyplot as plt
 
 # Function to call the Together API with the provided model
 def call_ai_model(all_message):
@@ -33,23 +35,25 @@ def call_ai_model(all_message):
     return response
 
 # Streamlit app layout
-st.title("Climate Change Impact on Sports Using AI")
+st.title("Impact of Climate on Sports Using AI")
 st.write("Predict and mitigate the impacts of climate change on sports performance and infrastructure.")
 
 # Input fields for user to enter data
-temperature = st.number_input("Enter temperature (°C):", min_value=-50, max_value=50, value=25)
-humidity = st.number_input("Enter humidity (%):", min_value=0, max_value=100, value=50)
-air_quality = st.number_input("Enter air quality index (AQI):", min_value=0, max_value=500, value=100)
-precipitation = st.number_input("Enter precipitation (mm):", min_value=0.0, max_value=500.0, value=10.0)
+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)
 
 if st.button("Generate Prediction"):
-    all_message = f"Predict the impact of the following climate conditions on sports performance and infrastructure: temperature {temperature}°C, humidity {humidity}%, air quality index {air_quality}, and precipitation {precipitation}mm."
+    all_message = (
+        f"Predict the impact on sports performance and infrastructure given the following climate conditions: "
+        f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, UV Index {uv_index}."
+    )
     
     try:
         with st.spinner("Generating response..."):
             response = call_ai_model(all_message)
             
-            # Handle the streaming response and concatenate chunks
             generated_text = ""
             for line in response.iter_lines():
                 if line:
@@ -65,21 +69,33 @@ if st.button("Generate Prediction"):
                     except json.JSONDecodeError:
                         continue
             
-            # Format the response for better readability
-            formatted_response = (
-                "<b>Impact of Climate Conditions on Sports Performance and Infrastructure:</b>\n"
-                f"- **Temperature**: {temperature}°C\n"
-                f"- **Humidity**: {humidity}%\n"
-                f"- **Air Quality Index**: {air_quality}\n"
-                f"- **Precipitation**: {precipitation}mm\n\n"
-                "<b>Findings:</b>\n"
-                f"{generated_text}\n\n"
-                "<b>Conclusion:</b>\n"
-                "Based on the predicted climate conditions, it's crucial to adapt sports performance strategies and infrastructure designs to ensure resilience and safety."
-            )
+            # Display concise response and conclusion
+            st.success("Response generated!")
 
-            st.success("Response generated successfully!")
-            st.markdown(formatted_response, unsafe_allow_html=True)
+            # Constructing the summary and conclusion
+            summary = f"**Impact Summary:** {generated_text.strip()}\n"
+            conclusion = "**Conclusion:** Proper adaptation to these climate conditions is essential for maintaining sports performance and infrastructure resilience."
+
+            # Display text
+            st.markdown(summary)
+            st.markdown(conclusion)
+
+            # Example data for charts
+            data = {
+                'Condition': ['Temperature', 'Humidity', 'Wind Speed', 'UV Index'],
+                'Value': [temperature, humidity, wind_speed, uv_index]
+            }
+            df = pd.DataFrame(data)
+
+            # Displaying a table
+            st.table(df)
+
+            # Plotting a bar chart
+            fig, ax = plt.subplots()
+            ax.bar(data['Condition'], data['Value'], color=['blue', 'green', 'orange', 'red'])
+            ax.set_ylabel('Value')
+            ax.set_title('Climate Condition Impact Indicators')
+            st.pyplot(fig)
     except ValueError as ve:
         st.error(f"Configuration error: {ve}")
     except requests.exceptions.RequestException as re: