Create src/fine_tune_helpers.py

src/fine_tune_helpers.py

@@ -1,39 +1,36 @@
 import pandas as pd
-from datasets import Dataset
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
+import logging
 
-def fine_tune_model(uploaded_file):
-    df = pd.read_csv(uploaded_file)
-    st.subheader("Dataset Preview")
-    st.write(df.head())
+def preprocess_data(dataset_path):
+    try:
+        data = pd.read_csv(dataset_path)
+        logging.info("Data loaded successfully")
+        
+        # Example preprocessing: clean data, handle missing values, etc.
+        data.dropna(inplace=True)
+        
+        return data
+    except Exception as e:
+        logging.error(f"Error during data preprocessing: {e}")
 
-    # Convert CSV to Hugging Face dataset format
-    dataset = Dataset.from_pandas(df)
-    
-    model_name = st.selectbox("Select model for fine-tuning", ["distilbert-base-uncased"])
-    
-    if st.button("Fine-tune Model"):
-        if model_name:
-            try:
-                model = AutoModelForSequenceClassification.from_pretrained(model_name)
-                tokenizer = AutoTokenizer.from_pretrained(model_name)
+def train_model(data, config):
+    try:
+        # Assuming some model training logic
+        model = "YourModel"  # Placeholder
+        logging.info("Model training started")
+        
+        # Configuration-based training
+        # Use hyperparameters from config
+        learning_rate = config.getfloat("model", "learning_rate")
+        
+        return model
+    except Exception as e:
+        logging.error(f"Error during model training: {e}")
 
-                def preprocess_function(examples):
-                    return tokenizer(examples['text'], truncation=True, padding=True)
-
-                tokenized_datasets = dataset.map(preprocess_function, batched=True)
-                
-                # Fine-tuning logic (example)
-                train_args = {
-                    "output_dir": "./results",
-                    "num_train_epochs": 3,
-                    "per_device_train_batch_size": 16,
-                    "logging_dir": "./logs",
-                }
-                
-                st.success("Fine-tuning started (demo)!")  # Fine-tuning process goes here
-            except Exception as e:
-                st.error(f"Error during fine-tuning: {e}")
-        else:
-            st.warning("Please select a model for fine-tuning.")
+def save_model(model):
+    try:
+        # Save the fine-tuned model
+        model.save("model_path")
+        logging.info("Model saved successfully")
+    except Exception as e:
+        logging.error(f"Error saving model: {e}")