--- a
+++ b/Streamlit/app.py
@@ -0,0 +1,173 @@
+import streamlit as st
+import requests
+import pandas as pd
+
+# Define the FastAPI endpoint
+url = "http://20.253.116.228/predict"
+
+# Style for the result
+st.markdown("""
+    <style>
+    .big-font {
+        font-size:30px !important;
+        font-weight: bold;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Function to translate prediction to human-readable form
+def interpret_prediction(prediction):
+    if prediction == 0:
+        return "Insuffient Weight"
+    elif prediction == 1:
+        return "Healthy Weight"
+    elif prediction == 2:
+        return "Overweight Level 1"
+    elif prediction == 3:
+        return "Overweight Level 2"
+    elif prediction == 4:
+        return "Obesity Level 1"
+    elif prediction == 5:
+        return "Obesity Level 2"
+    elif prediction == 6:
+        return "Obesity Level 3"
+    else:
+        return "Unknown Level"
+    
+
+def main():
+    st.title("Obesity Level Prediction")
+
+    # User can choose to input data via form or upload CSV
+    input_method = st.radio("How would you like to input data?", ('Fill Form', 'Upload CSV'))
+
+    if input_method == 'Fill Form':
+         # Initialize form fields with default values
+        form_data = {
+            "id": 0,
+            "Gender": 'Male',
+            "Age": 25.0,
+            "Height": 1.75,
+            "Weight": 70.0,
+            "family_history_with_overweight": 'yes',
+            "FAVC": 'yes',
+            "FCVC": 2.0,
+            "NCP": 3.0,
+            "CAEC": 'Sometimes',
+            "SMOKE": 'no',
+            "CH2O": 2.0,
+            "SCC": 'no',
+            "FAF": 1.0,
+            "TUE": 2.0,
+            "CALC": 'Sometimes',
+            "MTRANS": 'Public_Transportation'
+        }
+
+        # Create form to input data or display data from CSV
+        with st.form(key='my_form'):
+            id_field = st.number_input('ID', value=form_data["id"])
+            gender = st.selectbox('Gender', ['Male', 'Female'], index=['Male', 'Female'].index(form_data["Gender"]))
+            age = st.number_input('Age', min_value=18.00, max_value=100.00, value=form_data["Age"])
+            height = st.number_input('Height (meters)', value=form_data["Height"])
+            weight = st.number_input('Weight (kg)', value=form_data["Weight"])
+            family_history_with_overweight = st.selectbox('Family history of overweight', ['yes', 'no'], index=['yes', 'no'].index(form_data["family_history_with_overweight"]))
+            favc = st.selectbox('Frequent consumption of high caloric food', ['yes', 'no'], index=['yes', 'no'].index(form_data["FAVC"]))
+            fcvc = st.number_input('Frequency of vegetables consumption', min_value=1.00, max_value=3.00, value=form_data["FCVC"])
+            ncp = st.number_input('Number of main meals', value=form_data["NCP"])
+            caec = st.selectbox('Consumption of food between meals', ['no', 'Sometimes', 'Frequently', 'Always'], index=['no', 'Sometimes', 'Frequently', 'Always'].index(form_data["CAEC"]))
+            smoke = st.selectbox('Smoking', ['yes', 'no'], index=['yes', 'no'].index(form_data["SMOKE"]))
+            ch2o = st.number_input('Consumption of water daily (liters)', value=form_data["CH2O"])
+            scc = st.selectbox('Calories consumption monitoring', ['yes', 'no'], index=['yes', 'no'].index(form_data["SCC"]))
+            faf = st.number_input('Physical activity frequency (times per week)', value=form_data["FAF"])
+            tue = st.number_input('Time using electronic devices (hours per day)', value=form_data["TUE"])
+            calc = st.selectbox('Consumption of alcohol', ['no', 'Sometimes', 'Frequently'], index=['no', 'Sometimes', 'Frequently'].index(form_data["CALC"]))
+            mtrans = st.selectbox('Transportation used', ['Automobile', 'Motorbike', 'Bike', 'Public_Transportation', 'Walking'], index=['Automobile', 'Motorbike', 'Bike', 'Public_Transportation', 'Walking'].index(form_data["MTRANS"]))
+            submit_button = st.form_submit_button(label='Predict Obesity Level')
+
+            # Handle form submission
+        if submit_button:
+            # Construct the request payload
+            data = {
+                "id": id_field,
+                "Gender": gender,
+                "Age": age,
+                "Height": height,
+                "Weight": weight,
+                "family_history_with_overweight": family_history_with_overweight,
+                "FAVC": favc,
+                "FCVC": fcvc,
+                "NCP": ncp,
+                "CAEC": caec,
+                "SMOKE": smoke,
+                "CH2O": ch2o,
+                "SCC": scc,
+                "FAF": faf,
+                "TUE": tue,
+                "CALC": calc,
+                "MTRANS": mtrans
+            }
+
+        # Send a post request to the server
+        response = requests.post(url, json=data)
+        if response.status_code == 200:
+            result = response.json()
+            # Interpret the prediction for the user
+            prediction = interpret_prediction(result.get('prediction', -1))
+            # Display the prediction result
+            st.markdown(f'<p class="big-font">Obesity Level: {prediction}</p>', unsafe_allow_html=True)
+        else:
+            st.error("Failed to get a valid response from the model.")
+
+    elif input_method == 'Upload CSV':
+        uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+        if uploaded_file is not None:
+            dataframe = pd.read_csv(uploaded_file)
+            # Create a new column for predictions in the dataframe
+            dataframe['Obesity Level'] = None
+            
+            # Iterate over the rows of the dataframe and make predictions
+            with st.spinner('Making predictions...'):
+                for index, row in dataframe.iterrows():
+                    # Convert the row to dictionary
+                    data = row.to_dict()
+                    print(data)
+                    # Remove the 'Obesity Level' key if present
+                    data.pop('Obesity Level', None)
+                    # Send a post request to the server
+                    response = requests.post(url, json=data)
+                    if response.status_code == 200:
+                        result = response.json()
+                        # Interpret the prediction for the user
+                        prediction = interpret_prediction(result.get('prediction', -1))
+                        # Update the dataframe with predictions
+                        dataframe.at[index, 'Obesity Level'] = prediction
+                    else:
+                        st.error(f"Failed to get a valid response from the model for row {index+1}: {response.text}")
+                        break  # Stop the loop if there is an error
+
+            # Only proceed if all predictions were successful
+            if not dataframe['Obesity Level'].isnull().any():
+                st.success('All predictions made successfully!')
+                # Display the dataframe with predictions
+                st.dataframe(dataframe)
+                # Allow the user to download the augmented CSV
+                st.download_button(
+                    label="Download CSV with predictions",
+                    data=dataframe.to_csv(index=False),
+                    file_name='predictions.csv',
+                    mime='text/csv'
+                )
+            else:
+                st.error(f"Failed to get a valid response from the model: {response.text}")
+
+if __name__ == "__main__":
+    main()
+
+
+        
+
+
+
+
+
+