Update app.py

app.py

@@ -1,17 +1,60 @@
 import gradio as gr
 from transformers import pipeline, Trainer, TrainingArguments, AutoModelForCausalLM, AutoTokenizer
 import torch
+import pandas as pd
 
 # Initialize model and tokenizer
 model_name = "huggingface/transformer_model"  # Replace with the actual model name
 model = AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-# Define Gradio interface
+# Define Gradio interface function
 def upload_and_finetune(file):
-    # Your fine-tuning code here
-    # Example: Load dataset, preprocess, and fine-tune model
-    return f"File {file.name} uploaded successfully!"
+    # Read the uploaded file (assuming it's a CSV for this example)
+    file_path = file.name
+    data = pd.read_csv(file_path)  # Update this if the file format is different
+    
+    # Preprocess the data (tokenization)
+    # This example assumes the dataset has a 'text' column that contains the training data.
+    texts = data['text'].tolist()
+    encodings = tokenizer(texts, truncation=True, padding=True, return_tensors="pt")
+
+    # Create a dataset and dataloader for training
+    class CustomDataset(torch.utils.data.Dataset):
+        def __init__(self, encodings):
+            self.encodings = encodings
+
+        def __len__(self):
+            return len(self.encodings['input_ids'])
+
+        def __getitem__(self, idx):
+            item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
+            return item
+
+    train_dataset = CustomDataset(encodings)
+
+    # Set up training arguments
+    training_args = TrainingArguments(
+        output_dir='./results',          # output directory
+        num_train_epochs=3,              # number of training epochs
+        per_device_train_batch_size=4,   # batch size for training
+        logging_dir='./logs',            # directory for storing logs
+    )
+
+    # Set up Trainer
+    trainer = Trainer(
+        model=model,                         # the model to be trained
+        args=training_args,                  # training arguments, defined above
+        train_dataset=train_dataset,         # training dataset
+    )
+
+    # Train the model
+    trainer.train()
+
+    # Save the fine-tuned model
+    model.save_pretrained('./fine_tuned_model')
+    
+    return f"File {file.name} uploaded and model fine-tuned successfully!"
 
 # Create Gradio interface with correct parameter
 interface = gr.Interface(