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Update app.py
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app.py
CHANGED
@@ -18,14 +18,74 @@ description = """
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Please give it 3 to 4 minutes for the model to load and Run , consider using Python code with less than 120 lines of code due to GPU constrainst
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"""
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css = """.toast-wrap { display: none !important } """
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examples=[
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# Note: We have removed default system prompt as requested by the paper authors [Dated: 13/Oct/2023]
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# Prompting style for Llama2 without using system prompt
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Please give it 3 to 4 minutes for the model to load and Run , consider using Python code with less than 120 lines of code due to GPU constrainst
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"""
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css = """.toast-wrap { display: none !important } """
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examples=[["""
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import pandas as pd
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import re
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import ast
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from code_bert_score import score
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import numpy as np
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def preprocess_code(source_text):
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def remove_comments_and_docstrings(source_code):
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source_code = re.sub(r'#.*', '', source_code)
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source_code = re.sub(r'(\'\'\'(.*?)\'\'\'|\"\"\"(.*?)\"\"\")', '', source_code, flags=re.DOTALL)
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return source_code
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pattern = r"```python\s+(.+?)\s+```"
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matches = re.findall(pattern, source_text, re.DOTALL)
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code_to_process = '\n'.join(matches) if matches else source_text
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cleaned_code = remove_comments_and_docstrings(code_to_process)
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return cleaned_code
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def evaluate_dataframe(df):
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results = {'P': [], 'R': [], 'F1': [], 'F3': []}
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for index, row in df.iterrows():
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try:
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cands = [preprocess_code(row['generated_text'])]
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refs = [preprocess_code(row['output'])]
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P, R, F1, F3 = score(cands, refs, lang='python')
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results['P'].append(P[0])
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results['R'].append(R[0])
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results['F1'].append(F1[0])
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results['F3'].append(F3[0])
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except Exception as e:
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print(f"Error processing row {index}: {e}")
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for key in results.keys():
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results[key].append(None)
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df_metrics = pd.DataFrame(results)
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return df_metrics
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def evaluate_dataframe_multiple_runs(df, runs=3):
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all_results = []
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for run in range(runs):
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df_metrics = evaluate_dataframe(df)
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all_results.append(df_metrics)
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# Calculate mean and std deviation of metrics across runs
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df_metrics_mean = pd.concat(all_results).groupby(level=0).mean()
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df_metrics_std = pd.concat(all_results).groupby(level=0).std()
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return df_metrics_mean, df_metrics_std
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""" ] ,
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["""
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def analyze_sales_data(sales_records):
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active_sales = filter(lambda record: record['status'] == 'active', sales_records)
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sales_by_category = {}
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for record in active_sales:
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category = record['category']
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total_sales = record['units_sold'] * record['price_per_unit']
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if category not in sales_by_category:
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sales_by_category[category] = {'total_sales': 0, 'total_units': 0}
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sales_by_category[category]['total_sales'] += total_sales
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sales_by_category[category]['total_units'] += record['units_sold']
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average_sales_data = []
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for category, data in sales_by_category.items():
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average_sales = data['total_sales'] / data['total_units']
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sales_by_category[category]['average_sales'] = average_sales
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average_sales_data.append((category, average_sales))
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average_sales_data.sort(key=lambda x: x[1], reverse=True)
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for rank, (category, _) in enumerate(average_sales_data, start=1):
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sales_by_category[category]['rank'] = rank
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return sales_by_category
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"""]
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]
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# Note: We have removed default system prompt as requested by the paper authors [Dated: 13/Oct/2023]
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# Prompting style for Llama2 without using system prompt
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