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app.py

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+import gradio as gr
+import joblib
+from PIL import Image
+
+# Load already saved pipelines
+best_model_cs = joblib.load("modelCS.pkl")
+best_model_co2 = joblib.load("modelCO2.pkl")
+
+# Prediction function for compressive strength model
+def make_prediction(Fly_ash, GGBS_GGBFS, Fine_aggregate, Na2SiO3_solution, NaOH_solution, NaOH_concentration, Superplasticiser, Curing_temperature, Curing_Period, Testing_Age):
+    predt_cs = best_model_cs.predict([[Fly_ash, GGBS_GGBFS, Fine_aggregate, Na2SiO3_solution, NaOH_solution, NaOH_concentration, Superplasticiser, Curing_temperature, Curing_Period, Testing_Age]])
+    predt_co2 = best_model_co2.predict([[Fly_ash, GGBS_GGBFS, Fine_aggregate, Na2SiO3_solution, NaOH_solution, NaOH_concentration, Superplasticiser, Curing_temperature, Curing_Period, Testing_Age]])
+    value_cs = round(predt_cs[0], 3)
+    value_co2 = round(predt_co2[0], 3)
+    image_component = gr.Image("/Users/mohamedrabie/Library/CloudStorage/OneDrive-UniversityofWestLondon/University/Course/PhD/Research/PhD work/ML/Research work/Geopolymer mortar with ML/ML models and data/Models/MOO/MOO results.png", label="multi-objective optimization", type="pil")
+    return value_cs, value_co2, image_component, 
+
+# Inputs for the models
+inputs = [
+    gr.Slider(0, 961.0, label='Fly ash (Kg/m³)', info="Amount of Fly ash (Kg/m³)"),
+    gr.Slider(0, 711.0, label='GGBS or GGBFS (Kg/m³)', info="Amount of Ground Granulated Blast Furnace Slag(Kg/m³)"),
+    gr.Slider(0, 1956.0, label='Fine aggregate (Kg/m³)', info="Amount of Fine aggregate (Kg/m³)"),
+    gr.Slider(0, 531.0, label='Na₂SiO₃ solution (Kg/m³)', info="Amount of Sodium Silicate solution (Kg/m³)"),
+    gr.Slider(0, 284.44, label='NaOH solution (Kg/m³)', info="Amount of Sodium Hydroxide solution (Kg/m³)"),
+    gr.Slider(0, 16.0, label='NaOH concentration (M)', info="Sodium Hydroxide concentration (M)"),
+    gr.Slider(0, 35.555, label='Superplasticiser (kg/m³)', info="Amount of Superplasticiser (kg/m³)"),
+    gr.Slider(-7.5, 800.0, label='Curing temperature (°C)'),
+    gr.Slider(1, 180, label='Curing Period (days)'),
+    gr.Slider(1, 180, label='Testing Age (days)'),
+]
+
+# Outputs for both models
+outputs = [
+    gr.Textbox(label="Compressive strength (MPa)"),
+    gr.Textbox(label="CO₂ footprint (Kg/m³)"),
+    gr.Image(label="multi-objective optimization for geopolymer mortar"),
+]
+
+# Create one interface for both models
+app = gr.Interface(
+    fn=make_prediction,
+    inputs=inputs,
+    outputs=outputs,
+    title="Alkali-Activated Mortar Compressive Strength Prediction Using Machine Learning with Multi-objective Optimization",
+    description="Developed by Mohamed Rabieᵃ, Mohamed Aminᵇ, Usama Ebeadᵇ and Ibrahim Shaabanᵃ\n\nᵃUniversity of West London\n\nᵇQatar University \n\nContact: (mohamed.rabie@uwl.ac.uk ; mohamedrabie26@gmail.com)\n\nUse the sliders below to insert mix design quantity and click the submit button to make your prediction."
+)
+
+app.launch(share=False)

modelCO2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:05b5e70c90b47408ad358dff8251de7c7ade4016af461ff54df4e08fe4323a1b
+size 2340625

modelCS.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a2bf63f525af3b1f9fa7a67044ae696bc2caf0d8159fdb73d0c7c53c25e0ca9b
+size 7526317

requirements.txt

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+joblib
+scikit-learn==1.3.2
+gradio