fixed cas9off

app.py

@@ -132,10 +132,10 @@ if selected_model == 'Cas9':
 
             # prediction button
             if st.button('Predict off-target effects'):
-                if st.session_state.entry_method == 'manual':
+                if st.session_state.entry_method == ENTRY_METHODS['manual']:
                     user_input = st.session_state.manual_entry
                     predictions = cas9off.process_input_and_predict(user_input, input_type='manual')
-                elif st.session_state.entry_method == 'txt':
+                elif st.session_state.entry_method == ENTRY_METHODS['txt']:
                     uploaded_file = st.session_state.txt_entry
                     if uploaded_file is not None:
                         # Read the uploaded file content
@@ -143,6 +143,10 @@ if selected_model == 'Cas9':
                         predictions = cas9off.process_input_and_predict(file_content, input_type='manual')
 
                 st.session_state.off_target_results = predictions
+            else:
+                # Ensure that 'predictions' is defined even if the button hasn't been clicked
+                predictions = None
+
             progress = st.empty()
 
             # input error display

cas9off.py

@@ -82,9 +82,34 @@ class Encoder:
         self.on_off_code = np.array(on_off_dim7_codes)
 
 
+
+
+def encode_on_off_seq_pairs(input_file):
+    inputs = pd.read_csv(input_file, delimiter=",", header=None, names=['on_seq', 'off_seq'])
+    input_codes = []
+    for idx, row in inputs.iterrows():
+        on_seq = row['on_seq']
+        off_seq = row['off_seq']
+        en = Encoder(on_seq=on_seq, off_seq=off_seq)
+        input_codes.append(en.on_off_code)
+    input_codes = np.array(input_codes)
+    input_codes = input_codes.reshape((len(input_codes), 1, 24, 7))
+    y_pred = CRISPR_net_predict(input_codes)
+    inputs['CRISPR_Net_score'] = y_pred
+    inputs.to_csv("CRISPR_net_results.csv", index=False)
+
+def CRISPR_net_predict(X_test):
+    json_file = open("cas9_model/CRISPR_Net_CIRCLE_elevation_SITE_structure.json", 'r')
+    loaded_model_json = json_file.read()
+    json_file.close()
+    loaded_model = tf.keras.models.model_from_json(loaded_model_json)  # Updated for TensorFlow 2
+    loaded_model.load_weights("cas9_model/CRISPR_Net_CIRCLE_elevation_SITE_weights.h5")
+    y_pred = loaded_model.predict(X_test).flatten()
+    return y_pred
+
 def process_input_and_predict(input_data, input_type='manual'):
     """
-    Process the user input and predict using the CRISPR-Net model.
+    Process the user input and predict.
 
     Args:
     input_data: A string of manual input or the path to an input file.
@@ -119,29 +144,6 @@ def process_input_and_predict(input_data, input_type='manual'):
 
     return inputs
 
-def encode_on_off_seq_pairs(input_file):
-    inputs = pd.read_csv(input_file, delimiter=",", header=None, names=['on_seq', 'off_seq'])
-    input_codes = []
-    for idx, row in inputs.iterrows():
-        on_seq = row['on_seq']
-        off_seq = row['off_seq']
-        en = Encoder(on_seq=on_seq, off_seq=off_seq)
-        input_codes.append(en.on_off_code)
-    input_codes = np.array(input_codes)
-    input_codes = input_codes.reshape((len(input_codes), 1, 24, 7))
-    y_pred = CRISPR_net_predict(input_codes)
-    inputs['CRISPR_Net_score'] = y_pred
-    inputs.to_csv("CRISPR_net_results.csv", index=False)
-
-def CRISPR_net_predict(X_test):
-    json_file = open("cas9_model/CRISPR_Net_CIRCLE_elevation_SITE_structure.json", 'r')
-    loaded_model_json = json_file.read()
-    json_file.close()
-    loaded_model = tf.keras.models.model_from_json(loaded_model_json)  # Updated for TensorFlow 2
-    loaded_model.load_weights("cas9_model/CRISPR_Net_CIRCLE_elevation_SITE_weights.h5")
-    y_pred = loaded_model.predict(X_test).flatten()
-    return y_pred
-
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description="CRISPR-Net v1.0 (Aug 10 2019)")
     parser.add_argument("input_file",