Update app.py

app.py

@@ -1,63 +1,90 @@
+from datasets import load_dataset
+from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from transformers import pipeline
+import logging
+
+# Enable detailed logging
+logging.basicConfig(level=logging.INFO)
+
+# Load dataset
+dataset = load_dataset("mwitiderrick/swahili")
+
+# Print dataset columns for verification
+print(f"Dataset columns: {dataset['train'].column_names}")
+
+# Initialize the tokenizer and model
+model_name = "gpt2"  # Use GPT-2 for text generation
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+
+# Add a padding token to the tokenizer
+tokenizer.pad_token = tokenizer.eos_token  # Use eos_token as pad_token
+tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
+
+# Preprocess the dataset
+def preprocess_function(examples):
+    # Tokenize and format the dataset
+    encodings = tokenizer(
+        examples['text'],  # Use 'text' column from your dataset
+        truncation=True,
+        padding='max_length',  # Ensure consistent length
+        max_length=512
+    )
+    encodings['labels'] = encodings['input_ids']  # Use input_ids directly as labels
+    return encodings
+
+# Tokenize the dataset
+try:
+    tokenized_datasets = dataset.map(
+        preprocess_function, 
+        batched=True
+    )
+except Exception as e:
+    print(f"Error during tokenization: {e}")
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir='./results',
+    per_device_train_batch_size=4,
+    num_train_epochs=1,
+    logging_dir='./logs',
+    logging_steps=500,  # Log every 500 steps
+    evaluation_strategy="steps",  # Use evaluation strategy
+    save_steps=10_000,  # Save checkpoint every 10,000 steps
+    save_total_limit=2,  # Keep only the last 2 checkpoints
+)
+
+# Define Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    tokenizer=tokenizer,
 )
 
+# Start training
+try:
+    trainer.train()
+except Exception as e:
+    print(f"Error during training: {e}")
+
+# Define the Gradio interface function
+nlp = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def generate_text(prompt):
+    try:
+        return nlp(prompt, max_length=50)[0]['generated_text']
+    except Exception as e:
+        return f"Error during text generation: {e}"
+
+# Create and launch the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs="text",
+    outputs="text",
+    title="Swahili Language Model",
+    description="Generate text in Swahili using a pre-trained language model."
+)
 
-if __name__ == "__main__":
-    demo.launch()
+iface.launch()