Update app.py

app.py

@@ -2,8 +2,6 @@ import streamlit as st
 import requests
 import os
 import json
-import pandas as pd
-import matplotlib.pyplot as plt
 
 # Function to call the Together AI model
 def call_ai_model(all_message):
@@ -34,33 +32,6 @@ def call_ai_model(all_message):
 
     return response
 
-# Function to get performance data from AI
-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."
-    )
-    response = call_ai_model(all_message)
-    generated_text = ""
-    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:
-                        generated_text += delta["content"]
-            except json.JSONDecodeError:
-                continue
-
-    # Example: Replace with actual data from API
-    performance_score = 80  # Replace with actual data from API
-    return performance_score
-
 # Streamlit app layout
 st.title("Climate Impact on Sports Performance")
 st.write("Analyze and visualize the impact of climate conditions on sports performance.")
@@ -69,54 +40,37 @@ st.write("Analyze and visualize the impact of climate conditions on sports perfo
 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
-    }
+    all_message = (
+        f"Assess the impact on sports performance based on climate conditions: "
+        f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, "
+        f"UV Index {uv_index}, Air Quality Index {air_quality_index}, "
+        f"Precipitation {precipitation} mm, Atmospheric Pressure {atmospheric_pressure} hPa."
+    )
 
     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."
+                f"Analyze the impact on sports performance under the following conditions: "
+                f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, "
+                f"UV Index {uv_index}, Air Quality Index {air_quality_index}, "
+                f"Precipitation {precipitation} mm, Atmospheric Pressure {atmospheric_pressure} hPa."
             )
             qualitative_result = call_ai_model(qualitative_analysis)
 
-            # Get performance score for specified conditions
-            performance_score = get_performance_data(conditions)
-
         st.success("Predictions generated.")
 
         # Display qualitative analysis
         st.subheader("Qualitative Analysis")
         st.write(qualitative_result)
 
-        # Display performance score
-        st.subheader("Performance Score")
-        st.write(f"Predicted Performance Score: {performance_score}")
-
-        # Plotting the data
-        st.subheader("Performance Score vs Climate Conditions")
-
-        # Define climate conditions for plotting
-        climate_conditions = list(conditions.keys())
-        climate_values = list(conditions.values())
-
-        # Plotting performance score against climate conditions
-        fig, ax = plt.subplots()
-        ax.plot(climate_conditions, climate_values, marker='o', linestyle='-', color='b')
-        ax.set_xlabel('Climate Conditions')
-        ax.set_ylabel('Value')
-        ax.set_title('Performance Score vs Climate Conditions')
-        ax.grid(True)
-        st.pyplot(fig)
-
     except ValueError as ve:
         st.error(f"Configuration error: {ve}")
     except requests.exceptions.RequestException as re: