Create app-backup.py

app-backup.py

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+import fitz  # PyMuPDF for PDF extraction
+import re
+import unsloth
+import os
+from huggingface_hub import login
+from datasets import Dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
+from peft import LoraConfig, get_peft_model
+import gradio as gr
+from transformers import pipeline
+
+
+def extract_text_from_pdf(pdf_path):
+    """Extract text from a PDF file"""
+    doc = fitz.open(pdf_path)
+    text = "\n".join([page.get_text("text") for page in doc])
+    return text.strip()
+
+def preprocess_text(text):
+    """Basic text preprocessing"""
+    return re.sub(r"\s+", " ", text).strip()
+
+pdf_text = extract_text_from_pdf("new-american-standard-bible.pdf")
+clean_text = preprocess_text(pdf_text)
+
+
+# Read the Hugging Face token from environment variables
+hf_token = os.getenv("access_token")
+
+if hf_token is None:
+    raise ValueError("'access_token' is not set. Add it as a secret variable in Hugging Face Spaces.")
+
+# Log in to Hugging Face
+login(token=hf_token)
+
+#model_name = "meta-llama/Llama-2-7b-hf"  # You can use a smaller one like "meta-llama/Llama-2-7b-chat-hf"
+model_name = "unsloth/llama-2-7b-chat"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Create dataset
+data = {"text": [clean_text]}
+dataset = Dataset.from_dict(data)
+
+# Set a padding token manually
+tokenizer.pad_token = tokenizer.eos_token  # Use EOS as PAD token
+# Alternatively, add a new custom pad token
+# tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+# Tokenization function
+def tokenize_function(examples):
+    tokens = tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
+    tokens["labels"] = tokens["input_ids"].copy()  # Use input as labels for text generation
+    return tokens
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Load LLaMA 2 model in 4-bit mode to save memory
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    load_in_4bit=True,  # Use 4-bit quantization for efficiency
+    device_map="auto"
+    #device_map="cpu",
+    #quantization_config=None
+)
+
+# Apply LoRA (efficient fine-tuning)
+lora_config = LoraConfig(
+    r=8,  # Low-rank parameter
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],  # Applies only to attention layers
+    lora_dropout=0.05
+)
+
+model = get_peft_model(model, lora_config)
+
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="no",   # Disable evaluation (to enable, change value to 'epoch')
+    learning_rate=2e-4,
+    per_device_train_batch_size=1,  # Reduce batch size for memory efficiency
+    per_device_eval_batch_size=1,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    save_strategy="epoch",
+    logging_dir="./logs",
+    logging_steps=10,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets,
+    tokenizer=tokenizer,
+)
+
+def perform_training():
+    trainer.train()
+
+perform_training()
+
+model.save_pretrained("./fine_tuned_llama2")
+tokenizer.save_pretrained("./fine_tuned_llama2")
+
+
+# CHATBOT START
+chatbot = pipeline("text-generation", model="./fine_tuned_llama2")
+
+def chatbot_response(prompt):
+    result = chatbot(prompt, max_length=100, do_sample=True, temperature=0.7)
+    return result[0]["generated_text"]
+
+iface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text")
+iface.launch()