cleanup and test cases

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from run import tiger_predict, GUIDE_LEN, NUCLEOTIDE_TOKENS
+from tiger import tiger_predict, GUIDE_LEN, NUCLEOTIDE_TOKENS
 
 
 @st.cache

run.py → tiger.py

@@ -28,18 +28,20 @@ def process_data(x):
     return input_gens, model_input_x
 
 
-def gen_report_table(input_gens, res):
-    res = res.numpy().flatten().tolist()
-    # print("ftaltten res: ", res)
-    data = {"Gene": input_gens, "res": res}
-    tbl = pd.DataFrame.from_dict(data)
-    return tbl
-
-
-def tiger_predict(x):
-    input_gens, model_input_x = process_data(x)
-    # print("input gene: ", input_gens)
-    # print("model_input: ", model_input_x)
-    res = tiger.predict_step(model_input_x)
-    # print("res: ", res)
-    return gen_report_table(input_gens, res)
+def tiger_predict(transcript_seq: str):
+    # parse transcript sequence into 23-nt target sequences and their one-hot encodings
+    target_seq, target_seq_one_hot = process_data(transcript_seq)
+
+    # get predictions
+    normalized_lfc = tiger.predict_step(target_seq_one_hot)
+    predictions = pd.DataFrame({'Target site': target_seq, 'Normalized LFC': tf.squeeze(normalized_lfc).numpy()})
+
+    return predictions
+
+
+if __name__ == '__main__':
+
+    # simple test case
+    transcript_sequence = 'ACGTACGTACGTACGTACGTACGTACGTACGT'
+    df = tiger_predict(transcript_sequence)
+    print(df)