Update app.py

app.py

@@ -3,7 +3,6 @@ import requests
 import os
 import json
 import pandas as pd
-import time
 import matplotlib.pyplot as plt
 
 # Function to call the Together AI model
@@ -35,10 +34,14 @@ def call_ai_model(all_message):
 
     return response
 
-# Function to request numeric performance data from AI
-def get_numeric_performance_data(temperature):
+# Function to request performance data from AI
+def get_performance_data(conditions):
     all_message = (
-        f"Provide the expected numeric sports performance value (as a score) at a temperature of {temperature}°C."
+        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, UV Index {conditions['uv_index']}, "
+        f"Air Quality Index {conditions['air_quality_index']}, Precipitation {conditions['precipitation']} mm, "
+        f"Atmospheric Pressure {conditions['atmospheric_pressure']} hPa."
     )
     response = call_ai_model(all_message)
     generated_text = ""
@@ -51,15 +54,20 @@ def get_numeric_performance_data(temperature):
                 json_data = json.loads(line_content)
                 if "choices" in json_data:
                     delta = json_data["choices"][0]["delta"]
-                    if "content" in delta and delta["content"].strip().replace('.', '', 1).isdigit():
-                        return float(delta["content"].strip())
+                    if "content" in delta:
+                        generated_text += delta["content"]
             except json.JSONDecodeError:
                 continue
-    return None
+
+    try:
+        return float(generated_text.strip())
+    except ValueError:
+        st.warning(f"Could not convert the response to a float: {generated_text}")
+        return None
 
 # 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.")
+st.title("Climate Impact on Sports Performance")
+st.write("Analyze and visualize the impact of climate conditions on sports performance.")
 
 # Inputs for climate conditions
 temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25)
@@ -70,70 +78,57 @@ 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)
 
+# Button to generate predictions
 if st.button("Generate Prediction"):
-    all_message = (
-        f"Assess the impact on sports performance 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."
-    )
+    conditions = {
+        "temperature": temperature,
+        "humidity": humidity,
+        "wind_speed": wind_speed,
+        "uv_index": uv_index,
+        "air_quality_index": air_quality_index,
+        "precipitation": precipitation,
+        "atmospheric_pressure": atmospheric_pressure
+    }
 
     try:
-        with st.spinner("Analyzing climate conditions..."):
-            response = call_ai_model(all_message)
-
-        st.success("Initial analysis complete. Generating detailed predictions...")
-
-        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 and delta["content"].strip().replace('.', '', 1).isdigit():
-                            generated_text += delta["content"]
-                except json.JSONDecodeError:
-                    continue
-
-        st.success("Detailed predictions generated.")
-
-        # Prepare data for visualization
-        results_data = {
-            "Condition": ["Temperature", "Humidity", "Wind Speed", "UV Index", "Air Quality Index", "Precipitation", "Atmospheric Pressure"],
-            "Value": [temperature, humidity, wind_speed, uv_index, air_quality_index, precipitation, atmospheric_pressure]
-        }
-        results_df = pd.DataFrame(results_data)
-
-        # Display results in a table
-        st.subheader("Results Summary")
-        st.table(results_df)
-
-        # Display prediction
-        st.markdown("**Predicted Impact on Performance and Infrastructure:**")
-        st.markdown(generated_text.strip())
-
-        st.success("Generating performance data...")
-
-        # Generate numeric performance data for different temperatures
-        temperatures = range(-10, 41, 5)  # Temperatures from -10°C to 40°C in 5°C increments
-        performance_values = []
-        for temp in temperatures:
-            st.spinner(f"Fetching performance data for {temp}°C...")
-            performance_value = get_numeric_performance_data(temp)
-            if performance_value is not None:
-                performance_values.append(performance_value)
-            time.sleep(1)
-
-        if performance_values:
-            # Generate line graph
-            fig, ax = plt.subplots()
-            ax.plot(temperatures, performance_values, marker='o')
-            ax.set_xlabel('Temperature (°C)')
-            ax.set_ylabel('Performance Score')
-            ax.set_title('Temperature vs. Numeric Sports Performance')
+        with st.spinner("Generating predictions..."):
+            # Call AI model to get initial prediction and qualitative assessment
+            response = call_ai_model(f"Assess the impact on sports performance at conditions: "
+                                     f"Temperature {temperature}°C, Humidity {humidity}%, "
+                                     f"Wind Speed {wind_speed} km/h, UV Index {uv_index}, "
+                                     f"Air Quality Index {air_quality_index}, Precipitation {precipitation} mm, "
+                                     f"Atmospheric Pressure {atmospheric_pressure} hPa.")
+
+        st.success("Initial analysis complete.")
+
+        # Get performance score for specified conditions
+        performance_score = get_performance_data(conditions)
+        if performance_score is not None:
+            st.success("Performance data fetched successfully.")
+        else:
+            st.warning("Failed to fetch performance data.")
+
+        # Plotting the data
+        if performance_score is not None:
+            # Prepare data for plotting
+            climate_conditions = list(conditions.keys())
+            climate_values = list(conditions.values())
+
+            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_score], marker='s', color='r', label='Performance Score')
+            ax2.set_ylabel('Performance Score', color='r')
+            ax2.tick_params(axis='y', labelcolor='r')
+
+            fig.tight_layout()
             st.pyplot(fig)
 
     except ValueError as ve: