Delete updated_app.py

updated_app.py

@@ -1,272 +0,0 @@
-# updated_app.py
-# Enhanced Gradio app for Llama 4 Maverick healthcare fraud detection
-
-import gradio as gr
-from transformers import AutoProcessor, Llama4ForConditionalGeneration
-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 huggingface_hub
-import re
-import nltk
-from nltk.tokenize import sent_tokenize
-
-try:
-    nltk.data.find('tokenizers/punkt')
-except LookupError:
-    nltk.download('punkt')
-
-# Import the HealthcareFraudAnalyzer
-from document_analyzer import HealthcareFraudAnalyzer
-
-# Debug: Print environment variables to verify 'LLama' is present
-print("Environment variables:", dict(os.environ))
-
-# Retrieve the token from Hugging Face Space secrets
-LLama = os.getenv("LLama")
-if not LLama:
-    raise ValueError("LLama token not found. Set it in Hugging Face Space secrets as 'LLama'.")
-
-# Debug: Print token (first 5 chars for security, remove in production)
-print(f"Retrieved LLama token: {LLama[:5]}...")
-
-# Authenticate with Hugging Face
-huggingface_hub.login(token=LLama)
-
-# Model setup
-MODEL_ID = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"
-processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
-
-# Load model with FP8 quantization to fit in 80 GB VRAM
-model = Llama4ForConditionalGeneration.from_pretrained(
-    MODEL_ID,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    quantization_config={"load_in_8bit": True},
-    attn_implementation="flex_attention"
-)
-
-# 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 create training pairs from document text
-def extract_training_pairs_from_text(text):
-    pairs = []
-    patterns = [
-        # Medication patterns
-        (
-            r"(?i).*?\b(haloperidol|lorazepam|ativan)\b.*?\b(daily|routine|regular)\b.*?",
-            "Patient receives {} on a {} basis. Is this appropriate medication management?",
-            "This may indicate inappropriate medication management. Regular use of psychotropic medications without documented need assessment, behavior monitoring, and attempted dose reductions may violate care standards."
-        ),
-        # Documentation patterns
-        (
-            r"(?i).*?\b(missing|omitted|absent|lacking)\b.*?\b(documentation|records|logs|notes)\b.*?",
-            "Facility has {} {} for patient care. Is this a documentation concern?",
-            "Yes, incomplete documentation is a significant red flag. Missing records may indicate attempts to conceal care issues or fraudulent billing for services not provided."
-        ),
-        # Visitation patterns
-        (
-            r"(?i).*?\b(restrict|limit|prevent|block)\b.*?\b(visits|visitation|access|family)\b.*?",
-            "Facility {} family {} without documented medical necessity. Is this suspicious?",
-            "Yes, unjustified visitation restrictions may indicate attempts to conceal care issues and prevent family oversight. This can constitute fraud when facilities bill for care while violating resident rights."
-        ),
-        # Hospice patterns
-        (
-            r"(?i).*?\b(hospice|terminal|end.of.life)\b.*?\b(not|without|lacking)\b.*?\b(evidence|decline|documentation)\b.*?",
-            "Patient placed on {} care {} supporting {}. Is this fraudulent?",
-            "Yes, hospice enrollment without documented terminal decline may indicate Medicare fraud. Hospice certification requires genuine clinical determination of terminal status with prognosis of six months or less."
-        ),
-        # Contradictory documentation
-        (
-            r"(?i).*?\b(different|contradicts|conflicts|inconsistent)\b.*?\b(records|documentation|testimony|statements)\b.*?",
-            "Records show {} {} about patient condition. Is this fraudulent documentation?",
-            "Yes, contradictory documentation is a strong indicator of fraudulent record-keeping designed to misrepresent care quality or patient condition, particularly when official records differ from internal communications."
-        )
-    ]
-
-    for pattern, input_template, output_text in patterns:
-        matches = re.finditer(pattern, text)
-        for match in matches:
-            groups = match.groups()
-            if len(groups) >= 2:
-                input_text = input_template.format(*groups)
-                pairs.append({
-                    "input": input_text,
-                    "output": output_text
-                })
-
-    if not pairs:
-        if any(x in text.lower() for x in ["medication", "prescribed", "administered"]):
-            pairs.append({
-                "input": "Medication records show inconsistencies in administration times. Is this concerning?",
-                "output": "Yes, inconsistent medication administration timing may indicate fraudulent documentation or medication mismanagement that could harm patients."
-            })
-        if any(x in text.lower() for x in ["visit", "family", "spouse"]):
-            pairs.append({
-                "input": "Staff documents family visits inconsistently. Is this suspicious?",
-                "output": "Yes, selective documentation of family visits indicates fraudulent record-keeping designed to create a false narrative about family involvement and patient responses."
-            })
-        if any(x in text.lower() for x in ["hospice", "terminal", "prognosis"]):
-            pairs.append({
-                "input": "Patient remained on hospice for extended period without documented decline. Is this Medicare fraud?",
-                "output": "Yes, maintaining hospice services without documented decline suggests fraudulent hospice certification to obtain Medicare benefits inappropriately."
-            })
-
-    return pairs
-
-# Function to process uploaded files and train
-def train_ui(files):
-    try:
-        raw_text = ""
-        dataset = 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."
-
-        if raw_text:
-            training_data = extract_training_pairs_from_text(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")
-
-        def tokenize_data(example):
-            messages = [
-                {
-                    "role": "user",
-                    "content": [{"type": "text", "text": example['input']}]
-                },
-                {
-                    "role": "assistant",
-                    "content": [{"type": "text", "text": example['output']}]
-                }
-            ]
-            formatted_text = processor.apply_chat_template(messages, add_generation_prompt=False)
-            inputs = processor(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_args = TrainingArguments(
-            output_dir="./fine_tuned_llama4_healthcare",
-            per_device_train_batch_size=2,
-            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_llama4_healthcare")
-        processor.save_pretrained("./fine_tuned_llama4_healthcare")
-        return f"Training completed with {len(tokenized_dataset)} examples! Model saved to ./fine_tuned_llama4_healthcare"
-
-    except Exception as e:
-        return f"Error: {str(e)}. Please check file format, dependencies, or the LLama token."
-
-# Function to analyze uploaded document for fraud
-def analyze_document_ui(files):
-    try:
-        if not files:
-            return "Error: No file uploaded. Please upload a PDF to analyze."
-        
-        file = files[0]
-        if not file.name.endswith(".pdf"):
-            return "Error: Please upload a PDF file for analysis."
-        
-        raw_text = ""
-        with pdfplumber.open(file.name) as pdf:
-            for page in pdf.pages:
-                raw_text += page.extract_text() or ""
-        
-        if not raw_text:
-            return "Error: Could not extract text from the PDF. The file may be corrupt or contain only images."
-        
-        analyzer = HealthcareFraudAnalyzer(model, processor)
-        results = analyzer.analyze_document(raw_text)
-        return results["summary"]
-    
-    except Exception as e:
-        return f"Error during document analysis: {str(e)}"
-
-# Gradio UI with training and analysis tabs
-with gr.Blocks(title="Healthcare Fraud Detection Suite") as demo:
-    gr.Markdown("# Healthcare Fraud Detection Suite")
-    
-    with gr.Tabs():
-        with gr.TabItem("Fine-Tune Model"):
-            gr.Markdown("## Train Llama 4 for Healthcare Fraud Detection")
-            gr.Markdown("Upload PDFs (e.g., care logs, medication records) or a JSON file with training pairs.")
-            train_file_input = gr.File(label="Upload Files (PDF/JSON)", file_count="multiple")
-            train_button = gr.Button("Start Fine-Tuning")
-            train_output = gr.Textbox(label="Training Status", lines=5)
-            train_button.click(fn=train_ui, inputs=train_file_input, outputs=train_output)
-        
-        with gr.TabItem("Analyze Document"):
-            gr.Markdown("## Analyze Document for Healthcare Fraud Indicators")
-            gr.Markdown("Upload a PDF document to analyze for potential fraud, neglect, or abuse indicators.")
-            analyze_file_input = gr.File(label="Upload PDF Document")
-            analyze_button = gr.Button("Analyze Document")
-            analyze_output = gr.Markdown(label="Analysis Results")
-            analyze_button.click(fn=analyze_document_ui, inputs=analyze_file_input, outputs=analyze_output)
-    
-    gr.Markdown("""
-    ### About This Tool
-    This tool uses Llama 4 Maverick to identify patterns of potential fraud, neglect, and abuse in healthcare documentation.
-    The fine-tuning tab allows model customization with your examples or automatic extraction from documents.
-    The analysis tab scans documents for suspicious patterns, generating detailed reports.
-    **Note:** All analysis is performed locally - no data is shared externally.
-    """)
-
-# Launch the Gradio app
-demo.launch()