Limit the number of neighbors based on the dataframe

.gitignore

@@ -1,2 +1 @@
 __pycache__
-.venv

frontend/exploration.py

@@ -1,6 +1,5 @@
 import pandas as pd
 import streamlit as st
-import codecs
 
 st.set_page_config(
     page_title="Project Miner",
@@ -10,13 +9,10 @@ st.set_page_config(
 st.title("Home")
 
 ### Exploration
-uploaded_file = st.file_uploader("Upload your CSV file", type=["csv", "tsv"])
+uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
-separator = st.selectbox("Separator", [",", ";", "\\t"])
-separator = codecs.getdecoder("unicode_escape")(separator)[0]
-has_header = st.checkbox("Has header", value=True)
 
 if uploaded_file is not None:
-    st.session_state.data = pd.read_csv(uploaded_file, sep=separator, header=0 if has_header else 1)
+    st.session_state.data = pd.read_csv(uploaded_file)
     st.session_state.original_data = st.session_state.data
     st.success("File loaded successfully!")
 

frontend/pages/prediction_classification.py

@@ -1,64 +0,0 @@
-import streamlit as st
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
-from sklearn.preprocessing import LabelEncoder
-import pandas as pd
-
-st.header("Prediction: Classification")
-
-if "data" in st.session_state:
-    data = st.session_state.data
-
-    with st.form("classification_form"):
-        st.subheader("Classification Parameters")
-        data_name = st.multiselect("Features", data.columns)
-        target_name = st.selectbox("Target", data.columns)
-        test_size = st.slider("Test Size", min_value=0.1, max_value=0.5, value=0.2, step=0.1)
-        st.form_submit_button('Train and Predict')
-
-    if data_name and target_name:
-        X = data[data_name]
-        y = data[target_name]
-
-        label_encoders = {}
-        for column in X.select_dtypes(include=['object']).columns:
-            le = LabelEncoder()
-            X[column] = le.fit_transform(X[column])
-            label_encoders[column] = le
-        
-        if y.dtype == 'object':
-            le = LabelEncoder()
-            y = le.fit_transform(y)
-            label_encoders[target_name] = le
-
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
-
-        model = LogisticRegression()
-        model.fit(X_train, y_train)
-        y_pred = model.predict(X_test)
-        accuracy = accuracy_score(y_test, y_pred)
-
-        st.subheader("Model Accuracy")
-        st.write(f"Accuracy on test data: {accuracy:.2f}")
-
-        st.subheader("Enter values for prediction")
-        pred_values = []
-        for feature in data_name:
-            if feature in label_encoders:
-                values = list(label_encoders[feature].classes_)
-                value = st.selectbox(f"Value for {feature}", values)
-                value_encoded = label_encoders[feature].transform([value])[0]
-                pred_values.append(value_encoded)
-            else:
-                value = st.number_input(f"Value for {feature}", value=0.0)
-                pred_values.append(value)
-        
-        prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
-
-        if target_name in label_encoders:
-            prediction = label_encoders[target_name].inverse_transform(prediction)
-        
-        st.write("Prediction:", prediction[0])
-else:
-    st.error("File not loaded")

frontend/pages/prediction_regression.py

@@ -1,29 +0,0 @@
-import streamlit as st
-from sklearn.linear_model import LinearRegression
-import pandas as pd
-
-st.header("Prediction: Regression")
-
-if "data" in st.session_state:
-    data = st.session_state.data
-
-    with st.form("regression_form"):
-        st.subheader("Linear Regression Parameters")
-        data_name = st.multiselect("Features", data.select_dtypes(include="number").columns)
-        target_name = st.selectbox("Target", data.select_dtypes(include="number").columns)
-        st.form_submit_button('Train and Predict')
-
-    if data_name and target_name:
-        X = data[data_name]
-        y = data[target_name]
-
-        model = LinearRegression()
-        model.fit(X, y)
-        
-        st.subheader("Enter values for prediction")
-        pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
-        prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
-
-        st.write("Prediction:", prediction[0])
-else:
-    st.error("File not loaded")