Delete app.py

app.py

@@ -1,81 +0,0 @@
-import os
-import torch
-import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
-from peft import LoraConfig, get_peft_model, TaskType
-from huggingface_hub import HfApi, Repository
-from datasets import load_dataset
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-REPO_NAME = "tinyllama-lora-finetuned"
-
-# Initialize Streamlit
-st.title("🧑‍🏫 Python Tutor AI (Fine-tuned with LoRA)")
-
-# Create HF repo if it doesn't exist
-api = HfApi()
-api.create_repo(REPO_NAME, token=HF_TOKEN, repo_type="model", exist_ok=True)
-repo = Repository(local_dir=REPO_NAME, clone_from=f"hf://{REPO_NAME}", use_auth_token=HF_TOKEN)
-
-# Load TinyLlama Model & Tokenizer
-MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME,
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# LoRA Configuration
-lora_config = LoraConfig(
-    task_type=TaskType.CAUSAL_LM,
-    inference_mode=False,
-    r=8,
-    lora_alpha=32,
-    lora_dropout=0.1
-)
-
-# Apply LoRA to Model
-model = get_peft_model(model, lora_config)
-
-# Load dataset for fine-tuning
-dataset = load_dataset("Abirate/english_python_code_instructions", split="train[:2%]")
-
-# Fine-Tuning Parameters
-training_args = TrainingArguments(
-    output_dir="./results",
-    per_device_train_batch_size=1,
-    gradient_accumulation_steps=4,
-    optim="adamw_torch",
-    num_train_epochs=1,
-    logging_steps=10,
-    save_strategy="no"
-)
-
-# Trainer
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=dataset
-)
-
-# Fine-tune Model
-st.write("🎯 Fine-tuning Model (LoRA)...")
-trainer.train()
-st.success("✅ Fine-tuning complete!")
-
-# Push model to Hugging Face
-model.push_to_hub(REPO_NAME, use_auth_token=HF_TOKEN)
-tokenizer.push_to_hub(REPO_NAME, use_auth_token=HF_TOKEN)
-st.success("🚀 Model pushed to Hugging Face!")
-
-# User Input for Python Tutoring
-user_input = st.text_area("📝 Ask me a Python question:")
-if st.button("Get Answer"):
-    if user_input:
-        inputs = tokenizer(user_input, return_tensors="pt").to("cuda")
-        outputs = model.generate(**inputs, max_new_tokens=100)
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        st.write("💡 AI Tutor:", response)
-    else:
-        st.warning("⚠️ Please enter a question.")