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:", ("CSV Upload", "Manual Input")) # 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.")