Create fine_tune_helpers.py

fine_tune_helpers.py

@@ -0,0 +1,47 @@
+import pandas as pd
+from datasets import Dataset
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import streamlit as st
+
+def fine_tune_model(uploaded_file):
+    # Read CSV file
+    df = pd.read_csv(uploaded_file)
+    st.subheader("Dataset Preview")
+    st.write(df.head())
+    
+    # Check for a 'text' column or allow user to choose a column
+    if 'text' not in df.columns:
+        st.warning("No 'text' column found. Please select the column to use for fine-tuning.")
+        column_choice = st.selectbox("Select the column containing text data", df.columns)
+        df['text'] = df[column_choice]  # Create a 'text' column based on user selection
+
+    # 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 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.")