app.py

# app.py

import gradio as gr
from transformers import LlamaForCausalLM, LlamaTokenizer
import datasets
import torch
import json
import os
import pdfplumber
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from accelerate import Accelerator
import bitsandbytes
import sentencepiece
import huggingface_hub
from transformers import TrainingArguments, Trainer

# Debug: Print all environment variables to verify 'LLama' is present
print("Environment variables:", dict(os.environ))

# Retrieve the token from Hugging Face Space secrets
# Token placement: LLama:levi put token here
LLama = os.getenv("LLama")  # Retrieves the value of the 'LLama' environment variable
if not LLama:
    raise ValueError("LLama token not found in environment variables. Please set it in Hugging Face Space secrets under 'Settings' > 'Secrets' as 'LLama'.")

# Debug: Print the token to verify it's being read (remove this in production)
print(f"Retrieved LLama token: {LLama[:5]}... (first 5 chars for security)")

# Authenticate with Hugging Face
huggingface_hub.login(token=LLama)

# Model setup
MODEL_ID = "meta-llama/Llama-2-7b-hf"
tokenizer = LlamaTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)

# Load model with default attention mechanism (no Flash Attention)
model = LlamaForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    load_in_8bit=True
)

# Add padding token if it doesn't exist and resize embeddings
if tokenizer.pad_token is None:
    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
    model.resize_token_embeddings(len(tokenizer))

# Prepare model for LoRA training
model = prepare_model_for_kbit_training(model)
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"]
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

# Function to process uploaded files and train
def train_ui(files):
    try:
        # Process multiple PDFs or JSON
        raw_text = ""
        dataset = None  # Initialize dataset as None
        for file in files:
            if file.name.endswith(".pdf"):
                with pdfplumber.open(file.name) as pdf:
                    for page in pdf.pages:
                        raw_text += page.extract_text() or ""
            elif file.name.endswith(".json"):
                with open(file.name, "r", encoding="utf-8") as f:
                    raw_data = json.load(f)
                    training_data = raw_data.get("training_pairs", raw_data)
                    with open("temp_fraud_data.json", "w", encoding="utf-8") as f:
                        json.dump({"training_pairs": training_data}, f)
                    dataset = datasets.load_dataset("json", data_files="temp_fraud_data.json")

        if not raw_text and not dataset:
            return "Error: No valid PDF or JSON data found."

        # Create training pairs from PDFs if no JSON
        if raw_text:
            def create_training_pairs(text):
                pairs = []
                if "Haloperidol" in text and "daily" in text.lower():
                    pairs.append({
                        "input": "Patient received Haloperidol daily. Is this overmedication?",
                        "output": "Yes, daily Haloperidol use without documented severe psychosis or failed alternatives may indicate overmedication, violating CMS guidelines."
                    })
                if "Lorazepam" in text and "frequent" in text.lower():
                    pairs.append({
                        "input": "Care logs show frequent Lorazepam use with a 90-day supply. Is this suspicious?",
                        "output": "Yes, frequent use with a large supply suggests potential overuse or mismanagement, a fraud indicator."
                    })
                return pairs
            training_data = create_training_pairs(raw_text)
            with open("temp_fraud_data.json", "w") as f:
                json.dump({"training_pairs": training_data}, f)
            dataset = datasets.load_dataset("json", data_files="temp_fraud_data.json")

        # Tokenization function
        def tokenize_data(example):
            formatted_text = f"<s>[INST] {example['input']} [/INST] {example['output']}</s>"
            inputs = tokenizer(formatted_text, padding="max_length", truncation=True, max_length=4096, return_tensors="pt")
            inputs["labels"] = inputs["input_ids"].clone()
            return {k: v.squeeze(0) for k, v in inputs.items()}

        tokenized_dataset = dataset["train"].map(tokenize_data, batched=True, remove_columns=dataset["train"].column_names)

        # Training setup
        training_args = TrainingArguments(
            output_dir="./fine_tuned_llama_healthcare",
            per_device_train_batch_size=4,
            gradient_accumulation_steps=8,
            eval_strategy="no",
            save_strategy="epoch",
            save_total_limit=2,
            num_train_epochs=5,
            learning_rate=2e-5,
            weight_decay=0.01,
            logging_dir="./logs",
            logging_steps=10,
            bf16=True,
            gradient_checkpointing=True,
            optim="adamw_torch",
            warmup_steps=100,
        )

        def custom_data_collator(features):
            return {
                "input_ids": torch.stack([f["input_ids"] for f in features]),
                "attention_mask": torch.stack([f["attention_mask"] for f in features]),
                "labels": torch.stack([f["labels"] for f in features]),
            }

        trainer = Trainer(
            model=model,
            args=training_args,
            train_dataset=tokenized_dataset,
            data_collator=custom_data_collator,
        )
        trainer.train()
        model.save_pretrained("./fine_tuned_llama_healthcare")
        tokenizer.save_pretrained("./fine_tuned_llama_healthcare")
        return "Training completed! Model saved to ./fine_tuned_llama_healthcare"

    except Exception as e:
        return f"Error: {str(e)}. Please check file format, dependencies, or the LLama token."

# Gradio UI
with gr.Blocks(title="Healthcare Fraud Detection Fine-Tuning") as demo:
    gr.Markdown("# Fine-Tune LLaMA 2 for Healthcare Fraud Analysis")
    gr.Markdown("Upload PDFs (e.g., care logs, medication records) or a JSON file with training pairs.")
    file_input = gr.File(label="Upload Files (PDF/JSON)", file_count="multiple")
    train_button = gr.Button("Start Fine-Tuning")
    output = gr.Textbox(label="Training Status", lines=5)
    train_button.click(fn=train_ui, inputs=file_input, outputs=output)

# Launch the Gradio app
demo.launch()