updated code

app.py

@@ -13,7 +13,7 @@ def convert_smiles_to_mol(smiles_list, checkbox):
     if checkbox == True:
         cdk = CDK(ignore_3D=False)
     else:
-        cdk = CDK()
+        cdk = CDK(ignore_3D=True)
     smiles_list = list(smiles_list.split(','))
     try:
         mols = [Chem.AddHs(Chem.MolFromSmiles(smiles)) for smiles in smiles_list]
@@ -29,7 +29,7 @@ def convert_smiles_to_mol(smiles_list, checkbox):
             return molfile,str(e)
 
     except Exception as e:
-        return str(e), str(e)
+        return str(e), None
 
 
 # Function to calculate Molecular Weight
@@ -50,7 +50,7 @@ def get_geometric_descriptors(smiles):
     try:
         mol = Chem.MolFromSmiles(smiles)
         if mol is None:
-            return "Invalid SMILES string"
+            return "Invalid SMILES string", None
         
         # Add hydrogens and compute 3D coordinates
         mol = Chem.AddHs(mol)
@@ -76,7 +76,7 @@ def get_geometric_descriptors(smiles):
         df = pd.DataFrame([geometric_descriptors])
         return df.T,img
     except Exception as e:
-        return str(e), str(e)
+        return str(e), None
 
 # Function to check if a substructure is present
 def check_substructure(smiles, substructure_smiles):
@@ -86,7 +86,7 @@ def check_substructure(smiles, substructure_smiles):
     
     # Check if the molecule is None (invalid SMILES)
     if molecule is None or substructure is None:
-        return "Error","Error","Invalid SMILES string provided."
+        return None,None,"Invalid SMILES string provided."
     
     # Use RDKit's HasSubstructMatch to check for the substructure
     val = molecule.HasSubstructMatch(substructure)
@@ -100,9 +100,9 @@ def check_substructure(smiles, substructure_smiles):
             img2 = Draw.MolToImage(sub_molecule)
             return img1, img2, "Substructure is present."
         except Exception as e:
-            return str(e), str(e), "Substructure is present."        
+            return None, None, "Substructure is present."        
     else:
-        return img1,"NO Image","Substructure is not present."
+        return img1,None,"Substructure is not present."
 
 def calculate_similarity(smiles1, smiles2):
     try:
@@ -176,7 +176,7 @@ with gr.Blocks(theme='earneleh/paris') as demo:
                 weight_output = gr.TextArea(label="Geometric Values", lines=8, show_copy_button=True)
                 calculate_button = gr.Button("Calculate")
             with gr.Column():
-                image_output = gr.Image(label="Molecular Structure", height=400, width=500)
+                image_output = gr.Image(label="Molecular Structure", height=400, width=500, interactive=True)
                 calculate_button.click(fn=get_geometric_descriptors, inputs=smiles_input_mw,outputs=[weight_output, image_output])
             
      with gr.Tab("Check Substructure"):
@@ -187,15 +187,15 @@ with gr.Blocks(theme='earneleh/paris') as demo:
                 substructure_output = gr.Label(label="Is Substructure Present?")
                 check_button = gr.Button("Check")
             with gr.Column():
-                image_output1 = gr.Image(label="Molecular Structure", height=350, width=500)
-                image_output2 = gr.Image(label="Sub_Molecular Structure", height=350, width=500)
+                image_output1 = gr.Image(label="Molecular Structure", height=350, width=500, interactive=True)
+                image_output2 = gr.Image(label="Sub_Molecular Structure", height=350, width=500, interactive=True)
                 check_button.click(fn=check_substructure, inputs=[smiles_input_sub, substructure_input], outputs=[image_output1, image_output2, substructure_output])
 
     
      with gr.Tab("Calculate Similarity"):
         smiles_input1 = gr.Textbox(label="SMILES 1")
         smiles_input2 = gr.Textbox(label="SMILES 2")
-        similarity_output = gr.Number(label="Similarity (Tanimoto)")
+        similarity_output = gr.Label(label="Similarity (Tanimoto)")
         calculate_button_sim = gr.Button("Calculate Similarity")
         calculate_button_sim.click(fn=calculate_similarity, inputs=[smiles_input1, smiles_input2], outputs=similarity_output)