Update app.py

app.py

@@ -1,8 +1,53 @@
-from transformers import pipeline
+import gradio as gr
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
 
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
+# Dataset loading (replace with your desired dataset)
+dataset = load_dataset("meta-llama/Meta-Llama-3.1-8B-Instruct-evals", "Meta-Llama-3.1-8B-Instruct-evals__arc_challenge__details")
 
-pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3.1-405B-Instruct")
-print(pipe(messages))
+# Model and tokenizer (replace with desired model)
+model_name = "mradermacher/llama-3-8b-gpt-4o-GGUF"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Training function (optional)
+def train_model(epochs=3):
+    training_args = TrainingArguments(
+        output_dir="output",  # Adjust output directory
+        per_device_train_batch_size=8,  # Adjust batch size
+        num_train_epochs=epochs,
+        evaluation_strategy="epoch",  # Adjust evaluation strategy
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=dataset,
+    )
+
+    trainer.train()
+    print("Model training complete!")
+
+# Text generation function
+def generate_text(prompt):
+    try:
+        input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+        output = model.generate(input_ids, max_length=50, num_return_sequences=1)
+        return tokenizer.decode(output[0], skip_special_tokens=True)
+    except Exception as e:
+        return f"Error generating text: {e}"
+
+# Gradio interface for text generation
+interface = gr.Interface(
+    fn=generate_text,
+    inputs="text",
+    outputs="text",
+    title="Text Generation with Trained Model",
+    description="Enter a prompt and get creative text generated by the model.",
+)
+
+# Train the model before launching the interface (optional)
+train_model()  # Uncomment to train before launching
+
+# Launch the Gradio interface
+interface.launch()