Update app.py

app.py

@@ -1,48 +1,80 @@
 import gradio as gr
 import pandas as pd
-from PIL import Image
+import matplotlib.pyplot as plt
 
-# Function to read CSV and show preview
-def read_csv(file):
-    df = pd.read_csv(file.name)
-    return df.head()  # Show only the first 5 rows for preview
+# Function to calculate BMI
+def calculate_bmi(weight, height):
+    height_in_meters = height / 100  # Convert cm to meters
+    bmi = weight / (height_in_meters ** 2)
+    
+    if bmi < 18.5:
+        category = "Underweight"
+    elif 18.5 <= bmi < 24.9:
+        category = "Normal weight"
+    else:
+        category = "Overweight"
+    
+    return bmi, category
 
-# Function to upload and display an image
-def upload_image(image_file):
-    img = Image.open(image_file.name)
-    return img
+# Function to estimate daily calorie intake based on activity level
+def calorie_intake(age, gender, weight, height, activity_level):
+    if gender == "Male":
+        bmr = 10 * weight + 6.25 * height - 5 * age + 5
+    else:
+        bmr = 10 * weight + 6.25 * height - 5 * age - 161
+    
+    activity_multiplier = {
+        "Sedentary": 1.2,
+        "Lightly active": 1.375,
+        "Moderately active": 1.55,
+        "Very active": 1.725,
+        "Super active": 1.9
+    }
+    
+    calories_needed = bmr * activity_multiplier[activity_level]
+    return calories_needed
 
-# Function to select CSV display options (Preview or Full View)
-def toggle_view(file, view_type):
+# Function to read the CSV file and calculate BMI & Calorie Intake for each user
+def process_csv(file):
     df = pd.read_csv(file.name)
-    if view_type == "Preview":
-        return df.head()  # Show the first 5 rows for preview
-    return df  # Show the entire data
-
-# Creating Gradio Interface
-with gr.Blocks() as demo:
-    # Create a menu bar
-    gr.HTML("""
-    <div style="background-color:#f5f5f5;padding:10px;border-radius:5px;">
-        <h3 style="margin:0;">CSV and Image Viewer App</h3>
-    </div>
-    """)
-    
-    # Image Upload Section
-    with gr.Row():
-        image_input = gr.File(label="Upload Image", type="filepath", file_count="single")
-        image_output = gr.Image(type="pil")
-        image_input.change(upload_image, inputs=image_input, outputs=image_output)
-
-    # CSV File Upload Section
-    with gr.Row():
-        file_input = gr.File(label="Upload CSV File", type="filepath")
-        file_output = gr.DataFrame()
+    results = []
+    
+    for _, row in df.iterrows():
+        name = row["Name"]
+        age = row["Age"]
+        gender = row["Gender"]
+        height = row["Height_cm"]
+        weight = row["Weight_kg"]
+        activity_level = row["Activity_Level"]
         
-        # Radio buttons to select CSV view type (Preview or Full)
-        view_selector = gr.Radio(label="View Type", choices=["Preview", "Full View"], value="Preview")
+        bmi, bmi_category = calculate_bmi(weight, height)
+        calories = calorie_intake(age, gender, weight, height, activity_level)
         
-        file_input.change(toggle_view, inputs=[file_input, view_selector], outputs=file_output)
-        view_selector.change(toggle_view, inputs=[file_input, view_selector], outputs=file_output)
+        results.append({
+            "Name": name,
+            "BMI": f"{bmi:.2f}",
+            "BMI Category": bmi_category,
+            "Daily Calorie Needs (kcal)": f"{calories:.0f}",
+        })
+    
+    return pd.DataFrame(results)
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Personal Health Tracker")
+    gr.Markdown("### Upload your CSV file to calculate BMI and daily calorie needs for each person.")
+    
+    # File Upload
+    file_upload = gr.File(label="Upload CSV File", type="file")
+    
+    # Output for the result
+    output = gr.DataFrame(headers=["Name", "BMI", "BMI Category", "Daily Calorie Needs (kcal)"])
+    
+    # Button to trigger processing
+    process_button = gr.Button("Process CSV")
+    
+    # Function trigger
+    process_button.click(fn=process_csv, inputs=[file_upload], outputs=[output])
 
+# Launch the app
 demo.launch()