Update app.py

app.py

@@ -1,55 +1,4 @@
-from transformers import AutoTokenizer, AutoModelForQuestionAnswering, Trainer, TrainingArguments
-from datasets import Dataset
-import requests
-
-pisyn = requests.get("https://raw.githubusercontent.com/Fixyres/FHeta/refs/heads/main/modules.json")
-data = [
-    {"question": "Какая твоя база данных модулей? И по какой базе ты ищешь все модули?", "answer": pisyn.text}
-]
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-model = AutoModelForQuestionAnswering.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-
-dataset = Dataset.from_dict(data)
-
-def preprocess_function(examples):
-    questions = examples["question"]
-    answers = examples["answer"]
-    inputs = tokenizer(questions, padding=True, truncation=True, return_tensors="pt")
-    with tokenizer.as_target_tokenizer():
-        labels = tokenizer(answers, padding=True, truncation=True, return_tensors="pt")
-    inputs["labels"] = labels["input_ids"]
-    return inputs
-
-tokenized_datasets = dataset.map(preprocess_function, batched=True)
-
-training_args = TrainingArguments(
-    output_dir="./results",
-    num_train_epochs=3,
-    per_device_train_batch_size=8,
-    logging_dir="./logs",
-    logging_steps=10,
-)
-
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=tokenized_datasets,
-)
-
-trainer.train()
-
-model.save_pretrained("./FHeta")
-tokenizer.save_pretrained("./FHeta")
-
-tokenizer = AutoTokenizer.from_pretrained("./FHeta")
-model = AutoModelForQuestionAnswering.from_pretrained("./FHeta")
 
-def get_answer(query):
-    inputs = tokenizer(query, return_tensors="pt")
-    outputs = model(**inputs)
-    answer = tokenizer.decode(outputs["logits"][0], skip_special_tokens=True)
-    return answer
 
 import gradio as gr
 from huggingface_hub import InferenceClient