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Model/saved_model/lr/compressive_strength.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:550f45334ed9f0e7a21bc7f480f195f18435e9272d3852bf0bfa2df9a9a344ce
-size 937

Model/saved_model/lr/elongation.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e9fe4f2045e678c286b66e7271a927f6532ab35774f71dab2f3a823d8fc846bf
-size 42049

Model/saved_model/lr/hardness.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7fb61f94fc1e0becb7fc1125c54e21bb47e63658c49a2f4d685d5c759c962816
-size 124771

Model/saved_model/lr/plasticity.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2523c8f36729ef800a9a9caea6b02c0a049ac012e4ce690115831891247eb065
-size 101525

Model/saved_model/lr/tensile_strength.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:caad1f7df03626cf820eb00ad032cefd90375544356488de3fb7f58071c880f6
-size 44408

Model/saved_model/lr/yield_strength.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:05516385450822deb25cf0d963021b3eb41503970e07a38f651b57eb77a26780
-size 54432

app.py

@@ -16,14 +16,6 @@ import joblib
 '''
 Get the path of all of the saved models.
 '''
-
-lr_compressive_strength = "./Model/saved_model/lr/compressive_strength.pkl"
-lr_elongation = "./Model/saved_model/lr/elongation.pkl"
-lr_hardness = "./Model/saved_model/lr/hardness.pkl"
-lr_plasticity ="./Model/saved_model/lr/plasticity.pkl"
-lr_tensile_strength ="./Model/saved_model/lr/tensile_strength.pkl"
-lr_yield_strength ="./Model/saved_model/lr/yield_strength.pkl"
-
 kmeans_ssl_compressive_strength = "./Model/saved_model/kmeans_ssl/compressive_strength.pkl"
 kmeans_ssl_elongation = "./Model/saved_model/kmeans_ssl/elongation.pkl"
 kmeans_ssl_hardness = "./Model/saved_model/kmeans_ssl/hardness.pkl"
@@ -91,17 +83,7 @@ def pred(Al, B, C, Co, Cr, Cu, Fe, Ga, Ge, Hf, Li, Mg, Mn, Mo, N, Nb, Ni, Sc, Si
     # print(Composition)
     # print(comp.values())
 
-    if operation=="Linear Regression":
-        #Using RandomForest to predict properties.
-        Hardness = predict_input(lr_hardness).predict(df)
-        YieldStrength=predict_input(lr_yield_strength).predict(df)
-        TensileStrength=predict_input(lr_tensile_strength).predict(df)
-        Elongation=predict_input(lr_elongation).predict(df)
-        CompressiveStrength=predict_input(lr_compressive_strength).predict(df)
-        Plasticity=predict_input(lr_plasticity).predict(df)
-        # print("1")
-
-    elif operation=="K-Means Semi-supervisor Model":
+    if operation=="K-Means Semi-supervisor Model":
         #Using KNN & RandomForest to predict properties.
         Hardness = predict_input(kmeans_ssl_hardness).predict(df)
         YieldStrength = predict_input(kmeans_ssl_yield_strength).predict(df)
@@ -109,7 +91,6 @@ def pred(Al, B, C, Co, Cr, Cu, Fe, Ga, Ge, Hf, Li, Mg, Mn, Mo, N, Nb, Ni, Sc, Si
         Elongation = predict_input(kmeans_ssl_elongation).predict(df)
         CompressiveStrength = predict_input(kmeans_ssl_compressive_strength).predict(df)
         Plasticity = predict_input(kmeans_ssl_plasticity).predict(df)
-        # print("2")
 
     elif operation == "GMM Semi-supervisor Model":
         # Using semi_supervisor Label Propagation to predict properties.
@@ -227,7 +208,7 @@ with gr.Blocks() as demo:
 
     # The section to select the Machine Learning Model.
     with gr.Row():
-        operation = gr.inputs.Radio(["Linear Regression","K-Means Semi-supervisor Model", "GMM Semi-supervisor Model"])
+        operation = gr.inputs.Radio(["K-Means Semi-supervisor Model", "GMM Semi-supervisor Model"])
 
     # The section to input the element ratio.
     with gr.Row():