quick change

app.py

@@ -7,7 +7,7 @@ import pandas as pd
 st.title('Ravens AI')
 
 # text input with label
-sequence = st.text_input('Enter Amino Acid Sequence')
+sequence = st.text_input('Enter Peptide')
 
 model_type = st.radio(
     "Choose Linear Epitope Classifier",
@@ -23,83 +23,83 @@ elif model_type == 'Linear T-Cells (MHC Class II Restriction)':
 elif model_type == 'Linear B-Cell':
     model = TFAutoModelForSequenceClassification.from_pretrained('bcell')
 
-propep = st.radio(
-    "Scan over an entire protein or run a peptide sequence?",
-    ('Protein', 'Peptide'))
-
-if propep == 'Peptide':
-    threshold = st.slider('Probability Threshold', 0.0, 1.0, 0.75)
-    
-    if st.button('Submit'):
-        locations = []
-        peptide_name = sequence
-        peptide = tokenizer(peptide_name, return_tensors="tf")
-        output = model(peptide)
-        locations.append([peptide_name, output.logits.numpy()[0][0]])
-        
-        locations = pd.DataFrame(locations, columns = ['Peptide', 'Probability'])
-
-        # display table with sequence and probability as the headers
-        def color_survived(x: float): # x between 0 and 1
-            # red to green scale based on x
-            # 0 -> red
-            # 0.5 -> clear
-            # 1 -> green
-
-            # red
-            if x < threshold:
-                r = 179
-                g = 40
-                b = 2
-            # green
-            else:
-                r = 18
-                g = 150
-                b = 6
-
-            return f'background-color: rgb({r}, {g}, {b})'
-
-        st.table(locations.style.applymap(color_survived, subset=['Probability']))
-    
-
-elif propep == 'Protein':
-    # windows length slider
-    length = st.slider('Window Length', 1, 50, 10)
-    threshold = st.slider('Probability Threshold', 0.0, 1.0, 0.75)
-
-    # submit button
-    if st.button('Submit'):
-        if(length > len(sequence)):
-            st.write("Please make sure that your window length is less than the sequence length!")
+# propep = st.radio(
+#     "Scan over an entire protein or run a peptide sequence?",
+#     ('Protein', 'Peptide'))
+
+# if propep == 'Peptide':
+threshold = st.slider('Probability Threshold', 0.0, 1.0, 0.5)
+
+if st.button('Submit'):
+    locations = []
+    peptide_name = sequence
+    peptide = tokenizer(peptide_name, return_tensors="tf")
+    output = model(peptide)
+    locations.append([peptide_name, output.logits.numpy()[0][0]])
+
+    locations = pd.DataFrame(locations, columns = ['Peptide', 'Probability'])
+
+    # display table with sequence and probability as the headers
+    def color_survived(x: float): # x between 0 and 1
+        # red to green scale based on x
+        # 0 -> red
+        # 0.5 -> clear
+        # 1 -> green
+
+        # red
+        if x < threshold:
+            r = 179
+            g = 40
+            b = 2
+        # green
         else:
-            # run model
-            locations = []
-            for i in range(len(sequence) - length + 1):
-                peptide_name = sequence[i:i+length]
-                peptide = tokenizer(peptide_name, return_tensors="tf")
-                output = model(peptide)
-                locations.append([peptide_name, output.logits.numpy()[0][0]])
+            r = 18
+            g = 150
+            b = 6
+
+        return f'background-color: rgb({r}, {g}, {b})'
+
+    st.table(locations.style.applymap(color_survived, subset=['Probability']))
+
+
+# elif propep == 'Protein':
+#     # windows length slider
+#     length = st.slider('Window Length', 1, 50, 10)
+#     threshold = st.slider('Probability Threshold', 0.0, 1.0, 0.75)
+
+#     # submit button
+#     if st.button('Submit'):
+#         if(length > len(sequence)):
+#             st.write("Please make sure that your window length is less than the sequence length!")
+#         else:
+#             # run model
+#             locations = []
+#             for i in range(len(sequence) - length + 1):
+#                 peptide_name = sequence[i:i+length]
+#                 peptide = tokenizer(peptide_name, return_tensors="tf")
+#                 output = model(peptide)
+#                 locations.append([peptide_name, output.logits.numpy()[0][0]])
             
-            locations = pd.DataFrame(locations, columns = ['Peptide', 'Probability'])
-
-            # display table with sequence and probability as the headers
-            def color_survived(x: float): # x between 0 and 1
-                # red to green scale based on x
-                # 0 -> red
-                # 0.5 -> clear
-                # 1 -> green
-
-                # red
-                if x < threshold:
-                    r = 179
-                    g = 40
-                    b = 2
-                # green
-                else:
-                    r = 18
-                    g = 150
-                    b = 6
-
-                return f'background-color: rgb({r}, {g}, {b})'
-
-            st.table(locations.style.applymap(color_survived, subset=['Probability']))
+#             locations = pd.DataFrame(locations, columns = ['Peptide', 'Probability'])
+
+#             # display table with sequence and probability as the headers
+#             def color_survived(x: float): # x between 0 and 1
+#                 # red to green scale based on x
+#                 # 0 -> red
+#                 # 0.5 -> clear
+#                 # 1 -> green
+
+#                 # red
+#                 if x < threshold:
+#                     r = 179
+#                     g = 40
+#                     b = 2
+#                 # green
+#                 else:
+#                     r = 18
+#                     g = 150
+#                     b = 6
+
+#                 return f'background-color: rgb({r}, {g}, {b})'
+
+#             st.table(locations.style.applymap(color_survived, subset=['Probability']))