55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
from linguistic_checker import LinguisticChecker
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import pandas as pd
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api_key = '25bda2c39c0f8ca0'
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api_secret = 'e0008b9b9727cb8ceea5a132dbe62495'
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assistant_id = "66bb09a84673b57506fe7bbd"
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checker = LinguisticChecker(api_key, api_secret, assistant_id)
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df = pd.read_excel('./data_src/pingcap_pipeline.xlsx')
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report = []
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crm_fields = ['客户业务场景','友商信息']
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for crm_field in crm_fields:
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# Randomly select 20 items from the DataFrame
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random_pick_20 = df.sample(n=20, random_state=42)
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# Reset the index of the randomly selected items
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random_pick_20 = random_pick_20.reset_index(drop=True)
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print(f"Randomly selected {len(random_pick_20)} items from the DataFrame.")
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# Iterate over the DataFrame
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for index, row in random_pick_20.iterrows():
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try:
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# Adjust these names if they're different in your actual file
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user_input = row[crm_field]
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# Check the input using the LinguisticChecker
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result = checker.check_input(crm_field, user_input)
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# result = 'ssss'
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# Append the results to the report list
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report.append({
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'商机ID':row['唯一性ID（必填）'],
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'CRM必填项': crm_field,
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'用户输入': user_input,
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'分析诊断': result
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})
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except Exception as e:
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print(f"Error processing row {index}: {str(e)}")
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result = f"Error processing row {index}: {str(e)}"
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# Convert the report list to a DataFrame
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report_df = pd.DataFrame(report)
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# Write the DataFrame to an Excel file
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output_file = 'crm_mandatory_fields_analysis.xlsx'
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report_df.to_excel(output_file, index=False)
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print(f"Analysis report has been saved to {output_file}")
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