Update README

README.md

@@ -58,7 +58,7 @@ contains all the needed dependencies.
 The following command creates the trains the Deep Learning model and shows the accuracy and AUC:
 
 ```bash
-poetry run python locimend/main.py train <data file> <label file>
+poetry run python src/main.py train <data file> <label file>
 ```
 
 - <data file>: FASTQ file containing the sequences with errors
@@ -69,7 +69,7 @@ Both files must contain the canonical and read simulated sequences in the same p
 A dataset is provided to train the model, in order to proceed execute the following command:
 
 ```bash
-poetry run python locimend/main.py train data/curesim-HVR.fastq data/HVR.fastq
+poetry run python src/main.py train data/curesim-HVR.fastq data/HVR.fastq
 ```
 
 
@@ -85,7 +85,7 @@ A trained model is provided, which can be used to infer the correct sequences. T
 The following command will infer the correct sequence, and print it:
 
 ```bash
-poetry run python locimend/main.py infer "<DNA sequence>"
+poetry run python src/main.py infer "<dna sequence>"
 ```
 
 #### REST API
@@ -100,12 +100,6 @@ The API can be accessed at http://localhost:8000, with either a GET or POST requ
 
 | Request | Endpoint | Payload |
 |:----:|:-----:|:-----:|
-| GET     | / | Sequence as a path parameter (in the URL) |
+| GET     | /<sequence>         | Sequence as a path parameter |
-| POST     | /| JSON |
+| POST     | /| JSON: {"sequence": "<sequence>"} |
-
-For a POST request the JSON must have the following structure:
-
-```json
-{"sequence": "<DNA sequence>"}
-```
 

locimend/main.py

@@ -1,8 +1,7 @@
 from asyncio import run
 from argparse import ArgumentParser, Namespace
-from time import time
 
-from locimend.model import infer_sequence, train_model
+from model import infer_sequence, train_model
 
 
 def parse_arguments() -> Namespace:
@@ -22,10 +21,7 @@ def parse_arguments() -> Namespace:
 
 async def execute_task(args):
     if args.task == "train":
-        start_time = time()
         train_model(data_file=args.data_file, label_file=args.label_file)
-        end_time = time()
-        print(f"Training time: {end_time - start_time}")
     else:
         prediction = await infer_sequence(sequence=args.sequence)
         print(f"Error-corrected sequence: {prediction}")