Update app.py

app.py

@@ -1,7 +1,272 @@
+# 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)}"
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 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.
+    """)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Launch the Gradio app
+demo.launch()