Update app.py

app.py

@@ -58,7 +58,7 @@ def get_performance_data(temperature):
     try:
         return float(generated_text.strip())
     except ValueError:
-        return generated_text.strip()
+        return None
 
 # Streamlit app layout
 st.title("Climate Impact on Sports Performance and Infrastructure")
@@ -88,70 +88,20 @@ facility_age = st.number_input("Facility Age (years):", min_value=0, max_value=1
 materials_used = st.text_input("Materials Used in Construction:")
 
 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. "
-        f"Location: Latitude {latitude}, Longitude {longitude}. "
-        f"Athlete (Age: {age}, Sport: {sport}), Facility (Type: {facility_type}, Age: {facility_age}, Materials: {materials_used})."
-    )
-
     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:
-                            generated_text += delta["content"]
-                except json.JSONDecodeError:
-                    continue
-
-        st.success("Detailed predictions generated. Preparing visualizations...")
-
-        # 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("Visualizations ready. Generating performance data...")
+            performance_values = []
 
-        # 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:
-            st.spinner(f"Fetching performance data for {temp}°C...")
-            performance_value = get_performance_data(temp)
-            if isinstance(performance_value, float):
-                performance_values.append(performance_value)
-            else:
-                st.warning(performance_value)
-            time.sleep(1)
+            for temp in range(-10, 41, 5):  # Temperatures from -10°C to 40°C in 5°C increments
+                performance_value = get_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.plot(range(-10, 41, 5), performance_values, marker='o')
             ax.set_xlabel('Temperature (°C)')
             ax.set_ylabel('Performance Score')
             ax.set_title('Temperature vs. Sports Performance')