Update app.py

app.py

@@ -40,25 +40,27 @@ def get_performance_data(temperature):
     all_message = (
         f"Provide the expected sports performance value (as a numerical score) at a temperature of {temperature}°C."
     )
-    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
-    try:
-        return float(generated_text.strip())
-    except ValueError:
-        return None
+    while True:
+        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
+        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")
@@ -66,42 +68,23 @@ st.write("Analyze and visualize the impact of climate conditions on sports perfo
 
 # 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)
-
-# Geographical 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)
-
-# Athlete-specific data
-age = st.number_input("Athlete Age:", min_value=0, max_value=100, value=25)
-sport = st.selectbox("Select Sport:", ["Running", "Cycling", "Swimming", "Football", "Basketball"])
-performance_history = st.text_area("Athlete Performance History:")
-
-# Infrastructure characteristics
-facility_type = st.selectbox("Facility Type:", ["Stadium", "Gymnasium", "Outdoor Field"])
-facility_age = st.number_input("Facility Age (years):", min_value=0, max_value=100, value=10)
-materials_used = st.text_input("Materials Used in Construction:")
 
 if st.button("Generate Prediction"):
     try:
-        with st.spinner("Analyzing climate conditions..."):
-            performance_values = []
+        with st.spinner("Generating predictions..."):
+            st.success("Predictions generated. Generating performance data...")
 
-            for temp in range(-10, 41, 5):  # Temperatures from -10°C to 40°C in 5°C increments
+            # 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)
-                if performance_value is not None:
-                    performance_values.append(performance_value)
+                performance_values.append(performance_value)
                 time.sleep(1)
 
-        if performance_values:
             # Generate line graph
             fig, ax = plt.subplots()
-            ax.plot(range(-10, 41, 5), performance_values, marker='o')
+            ax.plot(temperatures, performance_values, marker='o')
             ax.set_xlabel('Temperature (°C)')
             ax.set_ylabel('Performance Score')
             ax.set_title('Temperature vs. Sports Performance')