Update app.py

app.py

@@ -1,88 +1,46 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from ansys.mapdl.core import launch_mapdl
-# Launch ANSYS MAPDL instance
-mapdl = launch_mapdl()
-# Clear any previous session
-mapdl.clear()
-mapdl.prep7() # Enter preprocessor
-# Function to define and simulate the boring machine attachment
-def simulate_attachment(thickness):
-# Define material properties
-mapdl.mp("EX", 1, 2e11) # Elastic modulus (Pa)
-mapdl.mp("PRXY", 1, 0.3) # Poisson's ratio
-# Create geometry
-mapdl.block(0, 200, 0, 100, 0, thickness) # Rectangular block
-mapdl.cylind(0, 10, 50, 50, 0, thickness) # Cylindrical hole
-mapdl.vsubtract("ALL") # Subtract cylinder from block
-# Define element type and mesh
-mapdl.et(1, "SOLID185") # 3D structural element
-mapdl.esize(5) # Element size
-mapdl.vmesh("ALL") # Mesh all volumes
-# Apply boundary conditions
-mapdl.nsel("S", "LOC", "X", 0) # Select nodes on one end
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-mapdl.d("ALL", "ALL") # Fix all degrees of freedom
-mapdl.nsel("S", "LOC", "X", 200) # Select nodes on the other end
-mapdl.f("ALL", "FY", -5000) # Apply force
-# Solve
-mapdl.run("/SOLU") # Enter solution phase
-mapdl.antype("STATIC") # Static analysis
-mapdl.solve()
-mapdl.finish() # Finish solution
-# Post-process
-mapdl.post1()
-max_stress = mapdl.get_value("NODE", 0, "S", "EQV") # Equivalent stress
-max_def = mapdl.get_value("NODE", 0, "U", "SUM") # Total deformation
-# Save plots
-mapdl.post_processing.plot_nodal_solution(
-"S", "EQV", title=f"Stress Distribution (Thickness={thickness} mm)",
-savefig=f"stress_{thickness}.png"
-)
-mapdl.post_processing.plot_nodal_solution(
-"U", "SUM", title=f"Deformation (Thickness={thickness} mm)",
-savefig=f"deformation_{thickness}.png"
-)
-return max_stress, max_def
-# Parameter sweep for different thicknesses
-thickness_values = [20, 25, 30]
-results = []
-for thickness in thickness_values:
-max_stress, max_def = simulate_attachment(thickness)
-results.append({"Thickness (mm)": thickness, "Max Stress (Pa)": max_stress, "Max
-Deformation (mm)": max_def})
-# Visualize results
+import streamlit as st
 import pandas as pd
-results_df = pd.DataFrame(results)
-print(results_df)
-39/112
-Key Features
-1. Geometry Creation:
-Python creates the geometry of the attachment, including a rectangular block with a
-cylindrical hole.
-2. Material Properties:
-Elastic modulus and Poisson's ratio are assigned programmatically.
-3. Meshing:
-Element type SOLID185 and ne mesh control are applied.
-4. Boundary Conditions and Loading:
-Fixes one side and applies a distributed load on the opposite side.
-5. Result Extraction:
-Maximum stress and deformation are retrieved for each thickness.
-Results are saved as plots for stress distribution and deformation.
-6. Parameter Sweep:
-Thickness is varied (20 mm, 25 mm, 30 mm), and simulations are run for each case.
-# Plot results
-plt.figure(figsize=(10, 5))
-plt.plot(results_df["Thickness (mm)"], results_df["Max Stress (Pa)"], marker="o",
-label="Max Stress")
-plt.plot(results_df["Thickness (mm)"], results_df["Max Deformation (mm)"],
-marker="o", label="Max Deformation")
-plt.xlabel("Thickness (mm)")
-plt.ylabel("Value")
-plt.title("Effect of Thickness on Stress and Deformation")
-plt.legend()
-plt.grid()
-plt.savefig("results_plot.png")
-plt.show()
-# Exit MAPDL
-mapdl.exit()
+import pickle
+from models.optimizer import optimize_design
+
+# Load pre-trained model
+def load_model():
+    with open("models/defect_model.pkl", "rb") as file:
+        model = pickle.load(file)
+    return model
+
+# Predict defects
+def predict_defects(model, data):
+    predictions = model.predict(data)
+    return predictions
+
+st.title("Press Tool AI: Defect Prediction and Optimization")
+
+# File upload
+uploaded_file = st.file_uploader("Upload Design Parameters (CSV)", type="csv")
+if uploaded_file:
+    data = pd.read_csv(uploaded_file)
+    st.write("Uploaded Data:")
+    st.dataframe(data)
+
+    # Load AI model
+    model = load_model()
+    
+    # Predict defects
+    st.subheader("Defect Predictions:")
+    predictions = predict_defects(model, data)
+    data['Predicted Defects'] = predictions
+    st.dataframe(data)
+
+    # Optimize design
+    st.subheader("Optimized Parameters:")
+    optimized_data = optimize_design(data)
+    st.dataframe(optimized_data)
+
+    # Download results
+    st.download_button(
+        label="Download Results",
+        data=optimized_data.to_csv(index=False),
+        file_name="optimized_design.csv",
+        mime="text/csv",
+    )