amihai85 commited on
Commit
eceedce
·
verified ·
1 Parent(s): f91e7ec

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +30 -6
app.py CHANGED
@@ -1,10 +1,11 @@
 
1
  from datasets import load_dataset
2
  from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
3
 
4
  # Load the dataset
5
  dataset = load_dataset("json", data_files="dataset.jsonl")
6
 
7
- # Load the model and tokenizer
8
  model_name = "Salesforce/codegen-2B-multi"
9
  model = AutoModelForCausalLM.from_pretrained(model_name)
10
  tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -21,13 +22,14 @@ training_args = TrainingArguments(
21
  overwrite_output_dir=True,
22
  evaluation_strategy="epoch",
23
  learning_rate=5e-5,
24
- per_device_train_batch_size=4,
25
  num_train_epochs=3,
26
  save_strategy="epoch",
27
  logging_dir="./logs",
 
28
  )
29
 
30
- # Train the model
31
  trainer = Trainer(
32
  model=model,
33
  args=training_args,
@@ -35,7 +37,29 @@ trainer = Trainer(
35
  eval_dataset=tokenized_dataset["train"],
36
  )
37
 
 
38
  trainer.train()
39
- trainer.save_model("./fine_tuned_codegen")
40
- tokenizer.save_pretrained("./fine_tuned_codegen")
41
- print("Training complete. Model saved.")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
  from datasets import load_dataset
3
  from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
4
 
5
  # Load the dataset
6
  dataset = load_dataset("json", data_files="dataset.jsonl")
7
 
8
+ # Load the pre-trained model and tokenizer
9
  model_name = "Salesforce/codegen-2B-multi"
10
  model = AutoModelForCausalLM.from_pretrained(model_name)
11
  tokenizer = AutoTokenizer.from_pretrained(model_name)
 
22
  overwrite_output_dir=True,
23
  evaluation_strategy="epoch",
24
  learning_rate=5e-5,
25
+ per_device_train_batch_size=2,
26
  num_train_epochs=3,
27
  save_strategy="epoch",
28
  logging_dir="./logs",
29
+ logging_strategy="epoch",
30
  )
31
 
32
+ # Trainer setup
33
  trainer = Trainer(
34
  model=model,
35
  args=training_args,
 
37
  eval_dataset=tokenized_dataset["train"],
38
  )
39
 
40
+ # Train the model
41
  trainer.train()
42
+
43
+ # Save the fine-tuned model
44
+ trainer.save_model("./fine_tuned_model")
45
+ tokenizer.save_pretrained("./fine_tuned_model")
46
+
47
+ # Load the fine-tuned model for inference
48
+ fine_tuned_model = AutoModelForCausalLM.from_pretrained("./fine_tuned_model")
49
+ fine_tuned_tokenizer = AutoTokenizer.from_pretrained("./fine_tuned_model")
50
+
51
+ # Define a Gradio interface for testing the model
52
+ def generate_cypress_code(prompt):
53
+ inputs = fine_tuned_tokenizer(prompt, return_tensors="pt")
54
+ outputs = fine_tuned_model.generate(inputs["input_ids"], max_length=150, num_return_sequences=1)
55
+ return fine_tuned_tokenizer.decode(outputs[0], skip_special_tokens=True)
56
+
57
+ # Launch the Gradio interface
58
+ interface = gr.Interface(
59
+ fn=generate_cypress_code,
60
+ inputs="text",
61
+ outputs="text",
62
+ title="Cypress Test Generator",
63
+ description="Enter a description of the test you want to generate Cypress code for.",
64
+ )
65
+ interface.launch()