Add file upload and basic correlations.

compose.yml

@@ -0,0 +1,8 @@
+services:
+
+  app:
+    build: .
+    volumes:
+      - .:/app
+    ports:
+      - 8501:8501

requirements.txt

@@ -1,3 +1,4 @@
 altair
 pandas
-streamlit
+streamlit
+matplotlib

src/streamlit_app.py

@@ -1,40 +1,25 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+'# Mi primera predicción'
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+file = st.file_uploader("Sube tu archivo CSV", type=["csv"])
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+if file is not None:
+    df = pd.read_csv(file)
+    target = st.selectbox("¿Qué columna quieres que el modelo aprenda a predecir?", df.columns)
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+    df_clean = df.dropna()
+    X_raw = df_clean.drop(columns=[target])
+    y = df_clean[target]
+    X = pd.get_dummies(X_raw, drop_first=True)
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    if pd.api.types.is_numeric_dtype(y):
+        correlaciones = pd.concat([X, y], axis=1).corr()[target].sort_values(ascending=False).drop(target)
+        correlaciones
+        fig, ax = plt.subplots()
+        correlaciones.head(10).plot(kind='bar', ax=ax, color='teal')
+        st.pyplot(fig)
+    else:
+        st.info("La variable objetivo es texto, se usará análisis de clasificación.")