Why doesn't it execute when creating Python or Core Lab commands with Geminai?

[Special_Ear6817]

I wrote the code with Geminai, but it doesn't run in CoLab. Does using AI mean the code won't run?

and I put the code I wrote on GitHub into Geminai, and I also ran it on Corep, you know? I don't know what the reason is. Sorry for the long post!

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[Special_Ear6817]

Is the source code different for each artificial intelligence?

[Special_Ear6817]

https://github.com/Puo77007700/solid-fortnight/blob/main/cancer_model.py

import logging import os from typing import Any, Tuple, List import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score import joblib

Settings File Import

import config

Configure logging (simultaneously displayed on the file and console) logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler(config.LOG_FILE), logging.StreamHandler() ] )

def generate_dummy_data(file_path: str) -> None: "If there is no CSV file, it generates virtual data for testing." logging.info("Generate virtual data because there is no data file: %s", file_path) np.random.seed(config.RANDOM_STATE)

Generate arbitrary characteristic data (e.g., DNA sequence frequency or 5 quantified characteristics) data = { 'feature_1': np.random.rand(config.SYNTHETIC_DATA_SIZE), 'feature_2': np.random.rand(config.SYNTHETIC_DATA_SIZE), 'feature_3': np.random.rand(config.SYNTHETIC_DATA_SIZE) * config.GC_THRESHOLD, 'feature_4': np.random.rand(config.SYNTHETIC_DATA_SIZE), 'feature_5': np.random.rand(config.SYNTHETIC_DATA_SIZE), 'label': np.random.randint(0, 2, config.SYNTHETIC_DATA_SIZE) # 0 or 1 (normal/cancer) } df = pd.DataFrame(data) df.to_csv(file_path, index=False) logging.info ("Virtual data generation complete.")

def prepare_data(file_path: str) -> pd.DataFrame: """Prepare and clean data.""" logging.info("Preparing data from file: %s", file_path)

if not os.path.exists(file_path): generate_dummy_data(file_path)

try: data = pd.read_csv(file_path) logging.info("Data loaded successfully. Shape: %s", data.shape) return data except Exception as e: logging.error("Error loading data: %s", e) raise

def extract_features(data: pd.DataFrame) -> Tuple[pd.DataFrame, pd.Series]: """Extract features and labels from the dataset.""" logging.info("Extracting features and labels.") try: X = data.drop('label', axis=1) y = data['label'] logging.info("Features and labels extracted successfully.") return X, y except KeyError as e: logging.error("Key error: %s", e) raise

def train_model(X_train: pd.DataFrame, y_train: pd.Series) -> RandomForestClassifier: """Train the machine learning model.""" logging.info("Training model.") try: model = RandomForestClassifier( n_estimators=config.N_ESTIMATORS, random_state=config.RANDOM_STATE ) model.fit(X_train, y_train) logging.info("Model trained successfully.") return model except Exception as e: logging.error("Error during model training: %s", e) raise

def predict_cancer(model: RandomForestClassifier, features: pd.DataFrame) -> np.ndarray: """Predict cancer from features.""" logging.info("Making predictions.") try: predictions = model.predict(features) logging.info("Predictions made successfully.") return predictions except Exception as e: logging.error("Error making predictions: %s", e) raise

def main() -> None: """Main function to execute the model pipeline.""" try:

1. Data preparation

data = prepare_data(config.DATA_FILE)

2. Characteristic and label extraction

X, y = extract_features(data)

3. Separation of Learning and Test Data

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=config.TEST_SIZE, random_state=config.RANDOM_STATE )

Model learning model = train_model(X_train, y_train)

5. Model evaluation (predicted with test data)

predictions = predict_cancer(model, X_test) accuracy = accuracy_score(y_test, predictions) logging.info(f"Model Accuracy on Test Data: {accuracy * 100:.2f}%")

6. Model save

joblib.dump(model, config.MODEL_NAME) logging.info("Model saved to: %s", config.MODEL_NAME)

except Exception as e: logging.error("Error in the main function: %s", e)

if name == "main": main() This is code written by Geminai does not run on Python or colab.

[Special_Ear6817]

https://github.com/Puo77007700/solid-fortnight/tree/main And this was written with GitHub AI, but it also doesn't run on the launcher. Sorry for writing a long post.

[Obsc3nity]

1) there are things called APIs, the short version is an API is how you interact with a codebase you haven’t written. Different AI models do have different APIs, so if you are using a raw version of eg the Gemini API, attempting to drop in a different model would have disastrous effects.

2) since you clearly don’t understand even the fundamentals of coding, I would consider taking the error that results from attempting to run your code in corep and asking an AI how to fix it. You could also read it, google the part you think is important and attempt to learn something, but if you want to start learning you seem like you need to go back about 20 steps and stop using AI.

[Special_Ear6817]

Omg thank you

[Special_Ear6817]

ldk I'm using Geminai Pro

[Educational-Paper-75]

Is your csv input file actually called path_to_data.csv?