1

app.py

@@ -1,5 +1,5 @@
 from datasets import load_dataset
-from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TrainingArguments, Trainer
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TrainingArguments, Trainer, pipeline
 import torch
 import time
 import evaluate
@@ -18,41 +18,3 @@ st.set_page_config(
 login(token='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', add_to_git_credential=True)
 
 st.title("Code Generation")
-huggingface_dataset_name = "red1xe/code_instructions"
-dataset = load_dataset(huggingface_dataset_name)
-model_name='bigcode/starcoder'
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-original_model = AutoModelForCausalLM.from_pretrained(model_name)
-
-x = st.slider(label='Select a sample', min_value=0, max_value=1000, value=500, step=10)
-if st.button("Show Sample"):
-    index = x
-
-    input = dataset['test'][index]['input']
-    instruction = dataset['test'][index]['instruction']
-    output = dataset['test'][index]['output']
-
-    prompt = f"""
-    Answer the following question.
-
-    {input} {instruction}
-
-    Answer:
-    """
-
-    inputs = tokenizer(prompt, return_tensors='pt')
-    outputs = tokenizer.decode(
-        original_model.generate(
-            inputs["input_ids"],
-            max_new_tokens=200,
-        )[0],
-        skip_special_tokens=True
-    )
-
-    dash_line = '-'.join('' for x in range(100))
-    st.write(dash_line)
-    st.write(f'INPUT PROMPT:\n{prompt}')
-    st.write(dash_line)
-    st.write(f'BASELINE HUMAN SUMMARY:\n{output}\n')
-    st.write(dash_line)
-    st.write(f'MODEL GENERATION - ZERO SHOT:\n{outputs}')