Update app.py

app.py

@@ -1,44 +1,74 @@
 import streamlit as st
 import joblib
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
 from models.optimizer import optimize_design
-
-# Sidebar for user inputs
-with st.sidebar:
-    tool_width = st.number_input("Tool Width (mm)", value=50)
-    tool_height = st.number_input("Tool Height (mm)", value=20)
-
-# Display tool dimensions
-st.write(f"Tool dimensions: {tool_width}mm x {tool_height}mm")
-
 # Load pre-trained model
 model = joblib.load('models/defect_model.pkl')
-
-# Title
+# Page Title
+19/112
 st.title("AI-Driven Press Tool Defect Predictor & Optimizer")
-
-# Input Form
-with st.form("Input Parameters"):
-    # Form input fields
-    tool_width = st.number_input("Tool Width (mm)", value=50)
-    tool_height = st.number_input("Tool Height (mm)", value=20)
-    material_strength = st.number_input("Material Strength (MPa)", value=300)
-    press_force = st.number_input("Press Force (kN)", value=100)
-
-    # Submit button
-    submit = st.form_submit_button("Predict & Optimize")
-
-# Handle form submission
-if submit:
-    # Predict defect
-    input_data = [[tool_width, tool_height, material_strength, press_force]]
-    defect = model.predict(input_data)[0]
-    st.subheader("Predicted Defect")
-    st.write(f"Predicted Defect Type: **{defect}**")
-
-    # Optimize design
-    optimized_params = optimize_design(tool_width, tool_height, material_strength, press_force)
-    st.subheader("Optimized Design Parameters")
-    st.write(optimized_params)
-
-    # Completion message
-    st.success("Optimization and Prediction Complete!")
+# Sidebar: User Input Parameters
+st.sidebar.header("Input Parameters")
+tool_width = st.sidebar.slider("Tool Width (mm)", min_value=30, max_value=100,
+value=50, step=5)
+tool_height = st.sidebar.slider("Tool Height (mm)", min_value=10, max_value=50,
+value=20, step=2)
+material_strength = st.sidebar.slider("Material Strength (MPa)", min_value=200,
+max_value=500, value=300, step=20)
+press_force = st.sidebar.slider("Press Force (kN)", min_value=50, max_value=300,
+value=100, step=10)
+# Input Data Frame
+input_data = pd.DataFrame({
+'tool_width': [tool_width],
+'tool_height': [tool_height],
+'material_strength': [material_strength],
+'press_force': [press_force]
+})
+# Predict Defect
+defect = model.predict(input_data)[0]
+probabilities = model.predict_proba(input_data)[0]
+# Optimize Design
+optimized_params = optimize_design(tool_width, tool_height, material_strength,
+press_force)
+# Main Section: Results
+st.subheader("Defect Prediction and Optimization Results")
+# Prediction Result
+st.markdown(f"### Predicted Defect: **{defect}**")
+# Visualize Prediction Probabilities
+st.markdown("### Prediction Probabilities")
+fig, ax = plt.subplots()
+labels = model.classes_
+ax.bar(labels, probabilities, color='skyblue')
+ax.set_ylabel("Probability")
+ax.set_title("Defect Prediction Probabilities")
+20/112
+Enhancements Added
+1. Bar Chart for Defect Probabilities:
+Displays the condence level for each defect prediction.
+2. Real-Time Parameter Adjustment:
+Adjust parameters like tool width, height, material strength, and press force using
+sliders.
+Instantly see how predictions and optimizations are aected.
+3. Line Chart for Parameter Impact:
+Visualizes the relationship between tool width and defect probabilities.
+Similar visualizations can be added for other parameters.
+st.pyplot(fig)
+# Optimized Parameters
+st.markdown("### Optimized Design Parameters")
+st.json(optimized_params)
+# Dynamic Visualization: Adjusting Parameters
+st.markdown("### Parameter Impact on Predictions")
+impact_chart_data = []
+for w in range(30, 101, 10):
+temp_data = pd.DataFrame({
+'tool_width': [w],
+'tool_height': [tool_height],
+'material_strength': [material_strength],
+'press_force': [press_force]
+})
+prob = model.predict_proba(temp_data)[0]
+impact_chart_data.append((w, prob))
+impact_chart_df = pd.DataFrame(impact_chart_data, columns=["Tool Width", *labels])
+st.line_chart(impact_chart_df.set_index("Tool Width"))