Update app.py

app.py

@@ -35,34 +35,31 @@ def call_ai_model(all_message):
 
     return response
 
-# Function to generate performance data
-def generate_performance_data(temperature_range):
-    performance_data = []
-    for temp in temperature_range:
-        message = (
-            f"Generate performance data for a sport under the following condition: "
-            f"Temperature {temp}°C. Provide a single value for performance."
-        )
-        response = call_ai_model(message)
-
-        performance_value = ""
-        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:
-                            performance_value += delta["content"]
-                except json.JSONDecodeError:
-                    continue
-        
-        performance_data.append(float(performance_value.strip()))
-
-    return performance_data
+# Function to request numerical performance data from AI
+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:
+        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")
@@ -77,114 +74,86 @@ 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)
 
-# Geographic location input
+# 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"):
     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"After analyzing that, visualize the data in the best way possible, might be in a table, using a chart or any other way so that it could be easy to understand"
+        f"Location: Latitude {latitude}, Longitude {longitude}. "
+        f"Athlete (Age: {age}, Sport: {sport}), Facility (Type: {facility_type}, Age: {facility_age}, Materials: {materials_used})."
     )
 
     try:
-        placeholder = st.empty()
-
-        with placeholder.container():
-            st.info("Collecting climate data...")
-            time.sleep(1)
-            placeholder.empty()
-
-        with placeholder.container():
-            st.info("Analyzing temperature data...")
-            time.sleep(1)
-            placeholder.empty()
-
-        with placeholder.container():
-            st.info("Evaluating humidity levels...")
-            time.sleep(1)
-            placeholder.empty()
-
-        with placeholder.container():
-            st.info("Assessing wind conditions...")
-            time.sleep(1)
-            placeholder.empty()
+        with st.spinner("Analyzing climate conditions..."):
+            response = call_ai_model(all_message)
 
-        with placeholder.container():
-            st.info("Checking UV index...")
-            time.sleep(1)
-            placeholder.empty()
+        st.success("Initial analysis complete. Generating detailed predictions...")
 
-        with placeholder.container():
-            st.info("Measuring air quality..")
-            time.sleep(1)
-            placeholder.empty()
-
-        with placeholder.container():
-            st.info("Calculating precipitation effects...")
-            time.sleep(1)
-            placeholder.empty()
+        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
 
-        with placeholder.container():
-            st.info("Analyzing atmospheric pressure...")
+        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...")
+
+        # 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 performance_value is not None:
+                performance_values.append(performance_value)
             time.sleep(1)
-            placeholder.empty()
-
-        with st.spinner("Finalizing predictions..."):
-            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
-
-            st.success("Response 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())
-
-            # Generate performance data for different temperatures
-            temp_range = [temperature - 5, temperature, temperature + 5]
-            performance_values = generate_performance_data(temp_range)
-
-            # Create a dataframe for performance values
-            performance_df = pd.DataFrame({
-                "Temperature": temp_range,
-                "Performance": performance_values
-            })
-
-            # Generate a line chart to show the relationship between temperature and performance
+        if performance_values:
+            # Generate line graph
             fig, ax = plt.subplots()
-            ax.plot(performance_df["Temperature"], performance_df["Performance"], marker='o', label="Performance")
+            ax.plot(temperatures, performance_values, marker='o')
             ax.set_xlabel('Temperature (°C)')
-            ax.set_ylabel('Performance')
-            ax.set_title('Relationship Between Temperature and Sports Performance')
-            ax.legend()
+            ax.set_ylabel('Performance Score')
+            ax.set_title('Temperature vs. Sports Performance')
             st.pyplot(fig)
 
     except ValueError as ve: