Fixed hovertext

FFMI.py

@@ -25,13 +25,19 @@ def calculate_ffmi(fat_free_mass_kg, height_m):
     return ffmi
 
 def plot_ffmi_curves(height_m, gender):
-    # Define weight range (X-axis)
-    weight_kg_range = np.linspace(45, 160, 100, endpoint=True)  # weight in kg
+    """
+    Generate a plot of FFMI (Fat-Free Mass Index) curves based on weight 
+    and body fat percentage for a specified gender.
+
+    :param height_m: Height of the individual in meters [m].
+    :param gender: Gender of the individual ('Male' or 'Female'), which determines 
+                   the FFMI ranges used for categorization.
+    :return: A Plotly Figure object containing the scatter plot of FFMI curves.
+    """
+    weight_kg_range = np.linspace(45, 160, 100, endpoint=True)
+    body_fat_percentage_range = np.linspace(1, 50, 100, endpoint=True)
 
-    # Define body fat percentages (Y-axis)
-    body_fat_percentage_range = np.linspace(1, 50, 100, endpoint=True)  # body fat percentage
     if gender == "Male":
-        # Define FFMI ranges and their descriptions for mans
         ffmi_ranges = [
             {"min": 0, "max": 18, "description": "Below average", "color": "blue"},
             {"min": 18, "max": 20, "description": "Average", "color": "green"},
@@ -42,7 +48,6 @@ def plot_ffmi_curves(height_m, gender):
             {"min": 28, "max": float("inf"), "description": "FAKEE", "color": "black"}
         ]
     else:
-        # Define FFMI ranges and their descriptions for womens
         ffmi_ranges = [
             {"min": 0, "max": 15, "description": "Below average", "color": "blue"},
             {"min": 15, "max": 17, "description": "Average", "color": "green"},
@@ -53,27 +58,23 @@ def plot_ffmi_curves(height_m, gender):
             {"min": 25, "max": float("inf"), "description": "FAKEE", "color": "black"}
         ]
 
-
-
-    # Create plot
     fig = go.Figure()
 
-    # Calculate and plot curves for each FFMI range
     for ffmi_range in ffmi_ranges:
         weight_kg_values = []
         body_fat_values = []
+        ffmi_values = []
 
         for weight in weight_kg_range:
             for body_fat_percentage in body_fat_percentage_range:
                 fat_free_mass = calculate_fat_free_mass(weight, body_fat_percentage)
                 ffmi = calculate_ffmi(fat_free_mass, height_m)
 
-                # Check if the calculated FFMI falls within the current FFMI range
                 if ffmi_range["min"] <= ffmi < ffmi_range["max"]:
                     weight_kg_values.append(weight)
                     body_fat_values.append(body_fat_percentage)
+                    ffmi_values.append(ffmi)
 
-        # Add curve for the current FFMI range
         fig.add_trace(go.Scatter(
             x=weight_kg_values,
             y=body_fat_values,
@@ -82,10 +83,14 @@ def plot_ffmi_curves(height_m, gender):
             marker=dict(
                 color=ffmi_range.get("marker_color", ffmi_range["color"]),
                 size=5, opacity=0.6,
-                line=dict(color=ffmi_range.get("line_color", ffmi_range["color"]))),
-                ))
+                line=dict(color=ffmi_range.get("line_color", ffmi_range["color"]))
+            ),
+            hovertext=[f'Weight: {weight_kg:.2f}<br>BodyFat: {body_fat:.2f}<br>FFMI: {ffmi:.2f}' for ffmi,weight_kg, body_fat in 
+                       
+                       zip(ffmi_values, weight_kg_values, body_fat_values)],
+            hoverinfo='text'
+        ))
 
-    # Update layout
     fig.update_layout(
         title='FFMI Curves for Different Body Fat Percentages and Weight',
         xaxis_title='Weight (kg)',
@@ -95,8 +100,4 @@ def plot_ffmi_curves(height_m, gender):
         template='plotly_dark'
     )
 
-    # Show the interactive plot
     return fig
-
-
-

app.py

@@ -12,7 +12,6 @@ def main():
         height_m = st.number_input("Enter your height in meters:", value=1.75, min_value=1.10, max_value=2.3, step=0.01)
     with col2:
         gender = st.selectbox("Select your gender", ["Male", "Female"])
-
     # Plot FFMI curves
     if st.button("Visualize FFMI Curves"):
         try: