Spaces:
Sleeping
Sleeping
File size: 13,104 Bytes
304986b 4e9c6f6 304986b 234500f 304986b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 |
import streamlit as st
import pandas as pd
import joblib
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
# Set the title of the app
st.title("🤖 Robotic Grasp Robustness Predictor")
st.markdown("""
Welcome! This application predicts the **stability/robustness** of a robotic grasp based on sensor data.
You can either upload a CSV file with sensor features or input values manually. 🚀
""")
# Display robotics images at the top (ensure the URLs are accessible)
st.image("industrial_robotic_arm_for_automated.png",
caption="Robotic Arm 🦾", use_container_width=True)
# Load the saved model (make sure the model file 'model.pkl' is in the same directory)
@st.cache_resource
def load_model():
model = joblib.load("model.pkl")
return model
model = load_model()
# Sidebar for input selection
st.sidebar.header("Input Options")
input_method = st.sidebar.radio("Choose input method:", ("Custom / API Input","CSV Upload"))
# Define the list of features expected by the model.
FEATURES = [
"H1_F1J2_pos", "H1_F1J2_vel", "H1_F1J2_eff",
"H1_F1J3_pos", "H1_F1J3_vel", "H1_F1J3_eff",
"H1_F1J1_pos", "H1_F1J1_vel", "H1_F1J1_eff",
"H1_F3J1_pos", "H1_F3J1_vel", "H1_F3J1_eff",
"H1_F3J2_pos", "H1_F3J2_vel", "H1_F3J2_eff",
"H1_F3J3_pos", "H1_F3J3_vel", "H1_F3J3_eff",
"H1_F2J1_pos", "H1_F2J1_vel", "H1_F2J1_eff",
"H1_F2J3_pos", "H1_F2J3_vel", "H1_F2J3_eff",
"H1_F2J2_pos", "H1_F2J2_vel", "H1_F2J2_eff"
]
# CSV Upload Option
if input_method == "CSV Upload":
st.header("Upload CSV File 📁")
uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
if uploaded_file is not None:
try:
input_df = pd.read_csv(uploaded_file)
st.success("CSV file successfully loaded! ✅")
# Clean column names
input_df.columns = input_df.columns.str.strip()
missing_features = [col for col in FEATURES if col not in input_df.columns]
if missing_features:
st.error(f"Missing required feature(s): {', '.join(missing_features)} 😟")
else:
# Make predictions
predictions = model.predict(input_df[FEATURES])
input_df["Predicted Robustness"] = predictions
# Interactive histogram of predictions
fig_hist = px.histogram(input_df, x="Predicted Robustness", nbins=30,
title="Distribution of Predicted Robustness")
st.plotly_chart(fig_hist, use_container_width=True)
st.subheader("Predictions")
st.dataframe(input_df)
except Exception as e:
st.error(f"Error processing file: {e}")
# Manual Input Option
else:
st.header("Manual Input ✍️")
st.markdown("Enter the sensor values for prediction below:")
# Add a radio button for selecting the input mode within manual input
input_mode = st.radio("Select input mode:", ("Custom Input", "API Default Values"))
# Default API values (you can adjust these as appropriate)
default_values = {
"H1_F1J2_pos": 0.5, "H1_F1J2_vel": 0.0, "H1_F1J2_eff": 0.1,
"H1_F1J3_pos": 0.5, "H1_F1J3_vel": 0.0, "H1_F1J3_eff": 0.1,
"H1_F1J1_pos": 0.5, "H1_F1J1_vel": 0.0, "H1_F1J1_eff": 0.1,
"H1_F3J1_pos": 1.0, "H1_F3J1_vel": 0.0, "H1_F3J1_eff": 0.2,
"H1_F3J2_pos": 1.0, "H1_F3J2_vel": 0.0, "H1_F3J2_eff": 0.2,
"H1_F3J3_pos": 1.0, "H1_F3J3_vel": 0.0, "H1_F3J3_eff": 0.2,
"H1_F2J1_pos": 0.7, "H1_F2J1_vel": 0.0, "H1_F2J1_eff": 0.15,
"H1_F2J3_pos": 0.7, "H1_F2J3_vel": 0.0, "H1_F2J3_eff": 0.15,
"H1_F2J2_pos": 0.7, "H1_F2J2_vel": 0.0, "H1_F2J2_eff": 0.15,
}
# Container dictionary for all input values.
input_data = {}
# Create expandable sections for sensor groups.
with st.expander("F1 Joint Sensors"):
st.write("Sensors for Finger 1:")
input_data["H1_F1J2_pos"] = st.number_input("H1_F1J2_pos",
value=default_values["H1_F1J2_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F1J2_vel"] = st.number_input("H1_F1J2_vel",
value=default_values["H1_F1J2_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F1J2_eff"] = st.number_input("H1_F1J2_eff",
value=default_values["H1_F1J2_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
st.write("Sensors for Finger 3:")
input_data["H1_F1J3_pos"] = st.number_input("H1_F1J3_pos",
value=default_values["H1_F1J3_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F1J3_vel"] = st.number_input("H1_F1J3_vel",
value=default_values["H1_F1J3_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F1J3_eff"] = st.number_input("H1_F1J3_eff",
value=default_values["H1_F1J3_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
st.write("Sensors for Finger 1 (alternate):")
input_data["H1_F1J1_pos"] = st.number_input("H1_F1J1_pos",
value=default_values["H1_F1J1_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F1J1_vel"] = st.number_input("H1_F1J1_vel",
value=default_values["H1_F1J1_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F1J1_eff"] = st.number_input("H1_F1J1_eff",
value=default_values["H1_F1J1_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
with st.expander("F3 Joint Sensors"):
input_data["H1_F3J1_pos"] = st.number_input("H1_F3J1_pos",
value=default_values["H1_F3J1_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F3J1_vel"] = st.number_input("H1_F3J1_vel",
value=default_values["H1_F3J1_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F3J1_eff"] = st.number_input("H1_F3J1_eff",
value=default_values["H1_F3J1_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F3J2_pos"] = st.number_input("H1_F3J2_pos",
value=default_values["H1_F3J2_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F3J2_vel"] = st.number_input("H1_F3J2_vel",
value=default_values["H1_F3J2_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F3J2_eff"] = st.number_input("H1_F3J2_eff",
value=default_values["H1_F3J2_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F3J3_pos"] = st.number_input("H1_F3J3_pos",
value=default_values["H1_F3J3_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F3J3_vel"] = st.number_input("H1_F3J3_vel",
value=default_values["H1_F3J3_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F3J3_eff"] = st.number_input("H1_F3J3_eff",
value=default_values["H1_F3J3_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
with st.expander("F2 Joint Sensors"):
input_data["H1_F2J1_pos"] = st.number_input("H1_F2J1_pos",
value=default_values["H1_F2J1_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F2J1_vel"] = st.number_input("H1_F2J1_vel",
value=default_values["H1_F2J1_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F2J1_eff"] = st.number_input("H1_F2J1_eff",
value=default_values["H1_F2J1_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F2J3_pos"] = st.number_input("H1_F2J3_pos",
value=default_values["H1_F2J3_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F2J3_vel"] = st.number_input("H1_F2J3_vel",
value=default_values["H1_F2J3_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F2J3_eff"] = st.number_input("H1_F2J3_eff",
value=default_values["H1_F2J3_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F2J2_pos"] = st.number_input("H1_F2J2_pos",
value=default_values["H1_F2J2_pos"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F2J2_vel"] = st.number_input("H1_F2J2_vel",
value=default_values["H1_F2J2_vel"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
input_data["H1_F2J2_eff"] = st.number_input("H1_F2J2_eff",
value=default_values["H1_F2J2_eff"] if input_mode=="API Default Values" else 0.0,
format="%.5f")
# Predict button for manual input
if st.button("Predict 🏁"):
input_df = pd.DataFrame([input_data])
prediction = model.predict(input_df)
st.success(f"The predicted grasp robustness is: {prediction[0]:.3f}")
# Create a gauge chart using Plotly for visualization.
fig_gauge = go.Figure(go.Indicator(
mode="gauge+number",
value=prediction[0],
title={'text': "Grasp Robustness"},
gauge={
'axis': {'range': [0, 100]},
'bar': {'color': "darkblue"},
'steps': [
{'range': [0, 30], 'color': "red"},
{'range': [30, 70], 'color': "yellow"},
{'range': [70, 100], 'color': "green"}
],
}
))
st.plotly_chart(fig_gauge, use_container_width=True)
# Display feature importances if available
try:
feature_importances = model.named_steps['model'].feature_importances_
imp_df = pd.DataFrame({
'Feature': FEATURES,
'Importance': feature_importances
}).sort_values(by='Importance', ascending=False)
st.subheader("Feature Importances 📊")
fig_imp = px.bar(imp_df, x='Feature', y='Importance', title="Feature Importances")
st.plotly_chart(fig_imp, use_container_width=True)
except Exception as ex:
st.info("Feature importance is not available for this model.")
|