Upload app.py

app.py

@@ -183,113 +183,116 @@ def error(option):
 
 ### If you insert an image
 if uploaded_file is not None:
-    ## Controller header
-    
-    st.sidebar.markdown("<h1 style='text-align: center;'>Compound's filter</h1>",unsafe_allow_html=True)
-    ## Write the compound 
-    st.sidebar.markdown('''
-                    <h4 style='text-align: center;'>This controller sidebar is used to filter the compounds by the following features</h4>
-                    
-                    - Molecular weight : is the weight of a compound in grame per mol
-                    - LogP             : it measures how hydrophilic or hydrophobic  a compound is
-                    - NumDonnors       : number of chemical components that are able to deliver electrons to other chemical components
-                    - NumAcceptors       : number of chemical components that are able to accept electrons to other chemical components
-                    ''',unsafe_allow_html=True)
-    weight_cutoff = st.sidebar.slider(
-        label="Molecular weight",
-        min_value=0,
-        max_value=1000,
-        value=500,
-        step=10,
-        help="Look for compounds that have less or equal molecular weight than the value selected"
-    )
-    logp_cutoff = st.sidebar.slider(
-        label="LogP",
-        min_value=-10,
-        max_value=10,
-        value=5,
-        step=1,
-        help="Look for compounds that have less or equal logp than the value selected"
-    )
-    NumHDonors_cutoff = st.sidebar.slider(
-        label="NumHDonors",
-        min_value=0,
-        max_value=15,
-        value=5,
-        step=1,
-        help="Look for compounds that have less or equal donors weight than the value selected"
-    )
-    NumHAcceptors_cutoff = st.sidebar.slider(
-        label="NumHAcceptors",
-        min_value=0,
-        max_value=20,
-        value=10,
-        step=1,
-        help="Look for compounds that have less or equal acceptors weight than the value selected"
-    )
-    max_phase = st.sidebar.multiselect("Phase of the compound",
-         ['1','2', '3', '4'],
-         help=""" 
-         - Phase 1 : Phase I   of the compound in progress
-         - Phase 2 : Phase II  of the compound in progress
-         - Phase 3 : Phase III of the compound in progress
-         - Phase 4 : Approved compound 
-         """
-        )
-   
+
     #### Read an image
 
    
     imgdef = read_image(uploaded_file)
-	
-    ### Plot the input image
-    fig, ax = plt.subplots()
-    ax.imshow(imgdef,cmap="gray")
-    st.pyplot(fig=fig)
-    # Printing the possibility of having anomalies
-    st.markdown("<h3 style='text-align: center;'>Possibility of anomalies</h3>",unsafe_allow_html=True)
-    model = generatemodel(xrv.models.DenseNet,"densenet121-res224-mimic_ch") ### MIMIC MODEL+
-    model.eval()
-    pr = outputprob2(imgdef,model) 
-    
-    # Sort results by the descending probability order 
-    pr = dict( sorted(pr.items(), key=operator.itemgetter(1),reverse=True))
-    # Select the treatment 
-    option = st.sidebar.selectbox('Select the treatment you believe for these illness',list(pr.keys()))
-    col1,col2,col3 = st.columns((1,1,1))
-    cnt = 1
-    for (key,value) in pr.items():
-        if cnt%3==1:
-            col1.metric(label=key, value=str(cnt), delta=str(value))
-        if cnt%3==2:
-            col2.metric(label=key, value=str(cnt), delta=str(value))
-        if cnt%3==0:
-            col3.metric(label=key, value=str(cnt), delta=str(value))
-        cnt+=1
-        # temp = st.expander("Compunds to take care of {}".format(key))
-    #### Get the compounds for the anomaly selected
-    df = getdrugs(option,max_phase)
-    st.markdown("<h3 style='text-align: center;'>Compounds for {}</h3>".format(option),unsafe_allow_html=True)
-    ### If exists the compounds
-    if df is not None:
+else:
+    imgdef = read_image("example.dcm")
+## Controller header
+
+st.sidebar.markdown("<h1 style='text-align: center;'>Compound's filter</h1>",unsafe_allow_html=True)
+## Write the compound 
+st.sidebar.markdown('''
+                <h4 style='text-align: center;'>This controller sidebar is used to filter the compounds by the following features</h4>
                 
-                #### Filter dataframe by controllers
-                df_result = df[df["mol_weight"] < weight_cutoff]
-                df_result2 = df_result[df_result["Logp"] < logp_cutoff]
-                df_result3 = df_result2[df_result2["Donnors"] < NumHDonors_cutoff]
-                df_result4 = df_result3[df_result3["Acceptors"] < NumHAcceptors_cutoff]
+                - Molecular weight : is the weight of a compound in grame per mol
+                - LogP             : it measures how hydrophilic or hydrophobic  a compound is
+                - NumDonnors       : number of chemical components that are able to deliver electrons to other chemical components
+                - NumAcceptors       : number of chemical components that are able to accept electrons to other chemical components
+                ''',unsafe_allow_html=True)
+weight_cutoff = st.sidebar.slider(
+    label="Molecular weight",
+    min_value=0,
+    max_value=1000,
+    value=500,
+    step=10,
+    help="Look for compounds that have less or equal molecular weight than the value selected"
+)
+logp_cutoff = st.sidebar.slider(
+    label="LogP",
+    min_value=-10,
+    max_value=10,
+    value=5,
+    step=1,
+    help="Look for compounds that have less or equal logp than the value selected"
+)
+NumHDonors_cutoff = st.sidebar.slider(
+    label="NumHDonors",
+    min_value=0,
+    max_value=15,
+    value=5,
+    step=1,
+    help="Look for compounds that have less or equal donors weight than the value selected"
+)
+NumHAcceptors_cutoff = st.sidebar.slider(
+    label="NumHAcceptors",
+    min_value=0,
+    max_value=20,
+    value=10,
+    step=1,
+    help="Look for compounds that have less or equal acceptors weight than the value selected"
+)
+max_phase = st.sidebar.multiselect("Phase of the compound",
+        ['1','2', '3', '4'],
+        help=""" 
+        - Phase 1 : Phase I   of the compound in progress
+        - Phase 2 : Phase II  of the compound in progress
+        - Phase 3 : Phase III of the compound in progress
+        - Phase 4 : Approved compound 
+        """
+    )
+
+
+### Plot the input image
+fig, ax = plt.subplots()
+ax.imshow(imgdef,cmap="gray")
+st.pyplot(fig=fig)
+# Printing the possibility of having anomalies
+st.markdown("<h3 style='text-align: center;'>Possibility of anomalies</h3>",unsafe_allow_html=True)
+model = generatemodel(xrv.models.DenseNet,"densenet121-res224-mimic_ch") ### MIMIC MODEL+
+model.eval()
+pr = outputprob2(imgdef,model) 
+
+# Sort results by the descending probability order 
+pr = dict( sorted(pr.items(), key=operator.itemgetter(1),reverse=True))
+# Select the treatment 
+option = st.sidebar.selectbox('Anomaly',list(pr.keys()),help='Select the illness or anomaly you want to treat')
+col1,col2,col3 = st.columns((1,1,1))
+cnt = 1
+for (key,value) in pr.items():
+    if cnt%3==1:
+        col1.metric(label=key, value=str(cnt), delta=str(value))
+    if cnt%3==2:
+        col2.metric(label=key, value=str(cnt), delta=str(value))
+    if cnt%3==0:
+        col3.metric(label=key, value=str(cnt), delta=str(value))
+    cnt+=1
+    # temp = st.expander("Compunds to take care of {}".format(key))
+#### Get the compounds for the anomaly selected
+df = getdrugs(option,max_phase)
+st.markdown("<h3 style='text-align: center;'>Compounds for {}</h3>".format(option),unsafe_allow_html=True)
+### If exists the compounds
+if df is not None:
             
+            #### Filter dataframe by controllers
+            df_result = df[df["mol_weight"] < weight_cutoff]
+            df_result2 = df_result[df_result["Logp"] < logp_cutoff]
+            df_result3 = df_result2[df_result2["Donnors"] < NumHDonors_cutoff]
+            df_result4 = df_result3[df_result3["Acceptors"] < NumHAcceptors_cutoff]
+        
 
-                
-                if len(df_result4)==0:
-                   
-                    error(option)
-                else:
-                        raw_html = mols2grid.display(df_result,  mapping={"smiles": "SMILES","pref_name":"Name","Acceptors":"Acceptors","Donnors":"Donnors","Logp":"Logp","mol_weight":"mol_weight"},
-                        subset=["img","Name"],tooltip=["Name","Acceptors","Donnors","Logp","mol_weight"],tooltip_placement="top",tooltip_trigger="click hover")._repr_html_()
             
-                        components.html(raw_html, width=900, height=900, scrolling=True)
-    #### We do not find compounds for the anomaly 
-    else:
-        error(option)
+            if len(df_result4)==0:
+                
+                error(option)
+            else:
+                    raw_html = mols2grid.display(df_result,  mapping={"smiles": "SMILES","pref_name":"Name","Acceptors":"Acceptors","Donnors":"Donnors","Logp":"Logp","mol_weight":"mol_weight"},
+                    subset=["img","Name"],tooltip=["Name","Acceptors","Donnors","Logp","mol_weight"],tooltip_placement="top",tooltip_trigger="click hover")._repr_html_()
+        
+                    components.html(raw_html, width=900, height=900, scrolling=True)
+#### We do not find compounds for the anomaly 
+else:
+    error(option)