Update app.py

app.py

@@ -35,13 +35,54 @@ def call_ai_model(all_message):
 
     return response
 
-# Function to request numerical performance data from AI
-def get_performance_data(temperature):
+# Function to request numeric performance data from AI
+def get_numeric_performance_data(temperature):
     all_message = (
-        f"Provide the expected sports performance value (as a numerical score) at a temperature of {temperature}°C."
+        f"Provide the expected numeric sports performance value (as a score) at a temperature of {temperature}°C."
     )
-    while True:
-        response = call_ai_model(all_message)
+    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 and delta["content"].strip().replace('.', '', 1).isdigit():
+                        return float(delta["content"].strip())
+            except json.JSONDecodeError:
+                continue
+    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.")
+
+# Inputs for climate conditions
+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)
+
+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."
+    )
+
+    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:
@@ -52,42 +93,47 @@ def get_performance_data(temperature):
                     json_data = json.loads(line_content)
                     if "choices" in json_data:
                         delta = json_data["choices"][0]["delta"]
-                        if "content" in delta:
+                        if "content" in delta and delta["content"].strip().replace('.', '', 1).isdigit():
                             generated_text += delta["content"]
                 except json.JSONDecodeError:
                     continue
-        try:
-            performance_value = float(generated_text.strip())
-            return performance_value
-        except ValueError:
-            continue
 
-# 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.success("Detailed predictions generated.")
 
-# Inputs for climate conditions
-temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25)
+        # 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)
 
-if st.button("Generate Prediction"):
-    try:
-        with st.spinner("Generating predictions..."):
-            st.success("Predictions generated. Generating performance data...")
-
-            # Generate 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:
-                performance_value = get_performance_data(temp)
+        # 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)
+            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. Sports Performance')
+            ax.set_title('Temperature vs. Numeric Sports Performance')
             st.pyplot(fig)
 
     except ValueError as ve: