test

app.py

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+import gradio as gr
+import torch
+from datasets import Dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
+import pandas as pd
+from huggingface_hub import login
+
+def train_model(file, hf_token):
+    try:
+        # Login to Hugging Face
+        if not hf_token:
+            return "Please provide a Hugging Face token"
+        login(hf_token)
+        
+        # Load and prepare data
+        df = pd.read_csv(file.name)
+        dataset = Dataset.from_pandas(df)
+        
+        # Model setup
+        model_name = "facebook/opt-125m"
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        
+        # Training configuration
+        training_args = TrainingArguments(
+            output_dir="./results",
+            num_train_epochs=3,
+            per_device_train_batch_size=2,
+            learning_rate=3e-5,
+            save_strategy="epoch",
+            push_to_hub=True,
+            hub_token=hf_token
+        )
+        
+        # Initialize trainer
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=dataset,
+            tokenizer=tokenizer
+        )
+        
+        # Run training
+        trainer.train()
+        
+        return "Training completed successfully!"
+        
+    except Exception as e:
+        return f"Error occurred: {str(e)}"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=train_model,
+    inputs=[
+        gr.File(label="Upload your CSV file"),
+        gr.Textbox(label="Hugging Face Token", type="password")
+    ],
+    outputs="text",
+    title="Product Classifier Training",
+    description="Upload your CSV data to train a product classifier model."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==4.19.2
+transformers==4.37.2
+torch==2.1.2
+datasets==2.16.1
+pandas==2.2.0