Update app.py

app.py

@@ -1,17 +1,11 @@
-import os
-import sys
-import streamlit as st
+import gradio as gr
+import numpy as np
 import pandas as pd
-import pickle
-
-# Ensure the project root is in the Python path
-import sys
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from joblib import dump, load
+from ansys.mapdl.core import launch_mapdl
+import matplotlib.pyplot as plt
 import os
-import gradio as gr
-
-# Add the necessary paths
-sys.path.append(os.path.abspath("path_to_your_directory"))
-
 
 # ========== Train AI Models ==========
 def train_models():
@@ -154,53 +148,3 @@ interface = gr.Interface(
 
 # Launch the interface
 interface.launch()
-th.dirname(os.path.abspath(__file__)))
-
-# Import optimization logic
-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 using the loaded model
-def predict_defects(model, data):
-    predictions = model.predict(data)
-    return predictions
-
-def main():
-    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 pre-trained defect prediction model
-        model = load_model()
-
-        # Predict defects
-        st.subheader("Defect Predictions:")
-        predictions = predict_defects(model, data)
-        data['Predicted Defects'] = predictions
-        st.dataframe(data)
-
-        # Optimize design parameters
-        st.subheader("Optimized Parameters:")
-        optimized_data = optimize_design(data)
-        st.dataframe(optimized_data)
-
-        # Provide a download button for optimized results
-        st.download_button(
-            label="Download Optimized Results",
-            data=optimized_data.to_csv(index=False),
-            file_name="optimized_design.csv",
-            mime="text/csv",
-        )
-
-if __name__ == "__main__":
-    main()