feat: desenvolve app em streamlit

src/streamlit_app.py

@@ -1,40 +1,70 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+
 import streamlit as st
+import json
+import pandas as pd
+import numpy as np
+
+st.set_page_config(page_title="PetSet", layout="centered")
+
+st.markdown("<h1 style='text-align: center;'>🐾 PetSet – Your Dataset Pet</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center; font-size: 18px;'>Upload a LeRobot .json dataset and start caring for your pet!</p>", unsafe_allow_html=True)
+
+uploaded_file = st.file_uploader("📂 Upload your LeRobotDataset (.json)", type=["json"])
+
+if uploaded_file:
+    data = json.load(uploaded_file)
+    episodes = data.get("episodes", [])
+
+    if not episodes:
+        st.error("No episodes found in this file.")
+    else:
+        df = pd.DataFrame(episodes)
+        st.success(f"{len(episodes)} episodes loaded successfully!")
+
+        # Calcular atributos do pet
+        success_rate = df["success"].mean()
+        duration_std = df["duration"].std()
+        health = 100 - df['success'].value_counts().get(False, 0) * 10
+        energy = success_rate * 100
+        attention = max(0, 100 - duration_std * 10)
+
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric("❤️ Health", f"{int(health)}%")
+        with col2:
+            st.metric("⚡ Energy", f"{int(energy)}%")
+        with col3:
+            st.metric("🧠 Attention", f"{int(attention)}%")
+
+        st.markdown("---")
+        st.image("https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExZ2EyZWkyd2ZzMmFmd3FuNm1jY2Fjdm42a2p6dDh4cGc1dzNsb25uMSZlcD12MV9naWZzX3NlYXJjaCZjdD1n/VbnUQpnihPSIgIXuZv/giphy.gif", width=200, caption="Your Pet")
+
+        st.markdown("### 🛠 What would you like to do?")
+        col_a, col_b, col_c = st.columns(3)
+        with col_a:
+            feed = st.button("🍎 Feed Data")
+        with col_b:
+            heal = st.button("🩺 Heal Pet")
+        with col_c:
+            check = st.button("🧪 Check Quality")
+
+        if heal:
+            original_len = len(data["episodes"])
+            data["episodes"] = [ep for ep in episodes if ep.get("success", True)]
+            removed = original_len - len(data["episodes"])
+            st.success(f"Removed {removed} failed episodes.")
+
+        if check:
+            problems = df['success'].value_counts().get(False, 0)
+            st.markdown(f"<div style='padding: 15px; background-color: #fff3cd; border-radius: 10px;'><strong>⚠️ Found {problems} corrupted episodes!</strong></div>", unsafe_allow_html=True)
+
+        st.markdown("### 📦 Dataset Preview")
+        st.dataframe(df[["id", "success", "duration", "avg_accel"]])
+
+        st.markdown("---")
+        if st.button("💾 Download Cleaned Dataset"):
+            cleaned_json = json.dumps(data, indent=2)
+            st.download_button("Download JSON", cleaned_json, file_name="cleaned_dataset.json")
 
-"""
-# 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)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-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])),
-    ))
+else:
+    st.info("👆 Upload a dataset file to begin.")