Update main.py

main.py

@@ -1,56 +1,29 @@
-from fastapi import FastAPI
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-from transformers import BertTokenizer, BertForMaskedLM, Trainer, TrainingArguments
+import torch
+from transformers import RobertaForMaskedLM, RobertaTokenizer
+from fastapi import FastAPI, HTTPException
 
 app = FastAPI()
 
-# Initialize the tokenizer and model
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertForMaskedLM.from_pretrained("bert-base-uncased")
+# Load the pre-trained model and tokenizer
+model = RobertaForMaskedLM.from_pretrained('roberta-base')
+tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
 
-# Prepare the training data
+# Load your dataset, in this case "cyberpunk_lore.txt"
 with open("cyberpunk_lore.txt", "r") as f:
-    train_data = f.read()
-train_data = train_data.split("\n")
-train_data = [tokenizer.encode(text, return_tensors="pt") for text in train_data]
-
-# Define the training arguments
-training_args = TrainingArguments(
-    output_dir="./results",
-    per_device_train_batch_size=16,
-    save_steps=10_000,
-    save_total_limit=2,
-)
-
-# Create the trainer
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=train_data,
-    eval_dataset=train_data,
-)
-
-# Start the training
-trainer.train()
-
-# Save the fine-tuned model
-trainer.save_model('./results')
-
-# Load the fine-tuned model
-model = trainer.get_model()
-
-# Create the inference endpoint
-@app.post("/infer")
-def infer(input: str):
-    input_ids = tokenizer.encode(input, return_tensors="pt")
-    output = model(input_ids)[0]
-    return {"output": output}
-
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="/app/static/index.html", media_type="text/html")
-    
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="/app/static/index.html", media_type="text/html")
+    dataset = f.read()
+
+# Train the model on your dataset
+input_ids = torch.tensor([tokenizer.encode(dataset, add_special_tokens=True)])
+model.train()
+model.zero_grad()
+outputs = model(input_ids, labels=input_ids)
+loss, logits = outputs[:2]
+loss.backward()
+
+# Serve the model via FastAPI
+@app.post("/predict")
+def predict(prompt: str):
+    input_ids = torch.tensor([tokenizer.encode(prompt, add_special_tokens=True)])
+    outputs = model(input_ids)
+    generated_text = tokenizer.decode(outputs[0].argmax(dim=1).tolist()[0])
+    return {"generated_text": generated_text}