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import logging
import time
import traceback
from sklearn.feature_extraction.text import TfidfVectorizer
from litellm import OpenAI
from classifiers import TFIDFClassifier, LLMClassifier
from utils import load_data, validate_results
def update_api_key(api_key):
"""Update the OpenAI API key"""
global OPENAI_API_KEY, client
if not api_key:
return "API Key cannot be empty"
OPENAI_API_KEY = api_key
try:
client = OpenAI(api_key=api_key)
# Test the connection with a simple request
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "test"}],
max_tokens=5,
)
return f"API Key updated and verified successfully"
except Exception as e:
error_msg = str(e)
logging.error(f"API key update failed: {error_msg}")
return f"Failed to update API Key: {error_msg}"
def process_file(file, text_columns, categories, classifier_type, show_explanations):
"""Process the uploaded file and classify text data"""
# Initialize result_df and validation_report
result_df = None
validation_report = None
try:
# Load data from file
if isinstance(file, str):
df = load_data(file)
else:
df = load_data(file.name)
if not text_columns:
return None, "Please select at least one text column"
# Check if all selected columns exist
missing_columns = [col for col in text_columns if col not in df.columns]
if missing_columns:
return (
None,
f"Columns not found in the file: {', '.join(missing_columns)}. Available columns: {', '.join(df.columns)}",
)
# Combine text from selected columns
texts = []
for _, row in df.iterrows():
combined_text = " ".join(str(row[col]) for col in text_columns)
texts.append(combined_text)
# Parse categories if provided
category_list = []
if categories:
category_list = [cat.strip() for cat in categories.split(",")]
# Select classifier based on data size and user choice
num_texts = len(texts)
# If no specific model is chosen, select the most appropriate one
if classifier_type == "auto":
if num_texts <= 500:
classifier_type = "gpt4"
elif num_texts <= 1000:
classifier_type = "gpt35"
elif num_texts <= 5000:
classifier_type = "hybrid"
else:
classifier_type = "tfidf"
# Initialize appropriate classifier
if classifier_type == "tfidf":
classifier = TFIDFClassifier()
results = classifier.classify(texts, category_list)
elif classifier_type in ["gpt35", "gpt4"]:
if client is None:
return (
None,
"Erreur : Le client API n'est pas initialisé. Veuillez configurer une clé API valide dans l'onglet 'Setup'.",
)
model = "gpt-3.5-turbo" if classifier_type == "gpt35" else "gpt-4"
classifier = LLMClassifier(client=client, model=model)
results = classifier.classify(texts, category_list)
else: # hybrid
if client is None:
return (
None,
"Erreur : Le client API n'est pas initialisé. Veuillez configurer une clé API valide dans l'onglet 'Setup'.",
)
# First pass with TF-IDF
tfidf_classifier = TFIDFClassifier()
tfidf_results = tfidf_classifier.classify(texts, category_list)
# Second pass with LLM for low confidence results
llm_classifier = LLMClassifier(client=client, model="gpt-3.5-turbo")
results = []
low_confidence_texts = []
low_confidence_indices = []
for i, (text, tfidf_result) in enumerate(zip(texts, tfidf_results)):
if tfidf_result["confidence"] < 70: # If confidence is below 70%
low_confidence_texts.append(text)
low_confidence_indices.append(i)
results.append(None) # Placeholder
else:
results.append(tfidf_result)
if low_confidence_texts:
llm_results = llm_classifier.classify(
low_confidence_texts, category_list
)
for idx, llm_result in zip(low_confidence_indices, llm_results):
results[idx] = llm_result
# Create results dataframe
result_df = df.copy()
result_df["Category"] = [r["category"] for r in results]
result_df["Confidence"] = [r["confidence"] for r in results]
if show_explanations:
result_df["Explanation"] = [r["explanation"] for r in results]
# Validate results using LLM
validation_report = validate_results(result_df, text_columns, client)
return result_df, validation_report
except Exception as e:
error_traceback = traceback.format_exc()
return None, f"Error: {str(e)}\n{error_traceback}"
def export_results(df, format_type):
"""Export results to a file and return the file path for download"""
if df is None:
return None
# Create a temporary file
import tempfile
import os
# Create a temporary directory if it doesn't exist
temp_dir = "temp_exports"
os.makedirs(temp_dir, exist_ok=True)
# Generate a unique filename
timestamp = time.strftime("%Y%m%d-%H%M%S")
filename = f"classification_results_{timestamp}"
if format_type == "excel":
file_path = os.path.join(temp_dir, f"{filename}.xlsx")
df.to_excel(file_path, index=False)
else:
file_path = os.path.join(temp_dir, f"{filename}.csv")
df.to_csv(file_path, index=False)
return file_path
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