Create app.py

app.py

@@ -0,0 +1,102 @@
+import os
+import requests
+import joblib
+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, AutoModelForSequenceClassification
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+app = FastAPI()
+
+# Utility to download files if not present locally
+def download_file(url, dest):
+    if not os.path.exists(dest):
+        print(f"Downloading {url} to {dest}")
+        r = requests.get(url)
+        r.raise_for_status()
+        with open(dest, 'wb') as f:
+            f.write(r.content)
+    else:
+        print(f"File {dest} already exists.")
+
+# ----------- Setup for BERT QA model (Virtual Consultation) ------------
+
+qa_model_dir = "./bert_mini_squadv2_finetuned"
+os.makedirs(qa_model_dir, exist_ok=True)
+
+qa_files = {
+    "pytorch_model.bin": "https://huggingface.co/your-username/your-files-path/resolve/main/bert_mini_squadv2_finetuned/pytorch_model.bin",
+    "config.json": "https://huggingface.co/your-username/your-files-path/resolve/main/bert_mini_squadv2_finetuned/config.json",
+    "tokenizer_config.json": "https://huggingface.co/your-username/your-files-path/resolve/main/bert_mini_squadv2_finetuned/tokenizer_config.json",
+    "vocab.txt": "https://huggingface.co/your-username/your-files-path/resolve/main/bert_mini_squadv2_finetuned/vocab.txt",
+}
+
+for fname, furl in qa_files.items():
+    download_file(furl, os.path.join(qa_model_dir, fname))
+
+tokenizer_qa = AutoTokenizer.from_pretrained(qa_model_dir)
+model_qa = AutoModelForQuestionAnswering.from_pretrained(qa_model_dir)
+
+# ----------- Setup for Diabetes XGBoost Model (Risk Prediction) ------------
+
+diabetes_pkl_url = "https://huggingface.co/your-username/your-files-path/resolve/main/diabetes_xgboost_model.pkl"
+diabetes_pkl_path = "./diabetes_xgboost_model.pkl"
+download_file(diabetes_pkl_url, diabetes_pkl_path)
+diabetes_model = joblib.load(diabetes_pkl_path)
+
+# ----------- Setup for other features: load pretrained models directly ------------
+
+from transformers import pipeline
+
+# Monitoring & Alerts - Summarization using bert-mini finetuned on squad_v2
+monitoring_model_id = "prajjwal1/bert-mini"
+summarizer = pipeline("summarization", model=monitoring_model_id)
+
+# Personalized Simulation - Bio_ClinicalBERT sequence classifier
+personalized_model_id = "emilyalsentzer/Bio_ClinicalBERT"
+personalized_tokenizer = AutoTokenizer.from_pretrained(personalized_model_id)
+personalized_model = AutoModelForSequenceClassification.from_pretrained(personalized_model_id)
+
+# --- Pydantic models for request validation ---
+
+class QARequest(BaseModel):
+    question: str
+    context: str
+
+class RiskPredictionRequest(BaseModel):
+    features: list  # example: [age, bmi, blood_pressure, ...]
+
+# --- API endpoints ---
+
+@app.post("/virtual_consultation")
+def virtual_consultation(data: QARequest):
+    inputs = tokenizer_qa(data.question, data.context, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model_qa(**inputs)
+    answer_start = torch.argmax(outputs.start_logits)
+    answer_end = torch.argmax(outputs.end_logits) + 1
+    answer = tokenizer_qa.convert_tokens_to_string(
+        tokenizer_qa.convert_ids_to_tokens(inputs.input_ids[0][answer_start:answer_end])
+    )
+    return {"answer": answer}
+
+@app.post("/risk_prediction")
+def risk_prediction(data: RiskPredictionRequest):
+    import numpy as np
+    features = np.array(data.features).reshape(1, -1)
+    pred = diabetes_model.predict(features)
+    return {"risk_prediction": int(pred[0])}
+
+@app.post("/monitoring_alerts")
+def monitoring_alerts(text: str):
+    summary = summarizer(text, max_length=50, min_length=20, do_sample=False)
+    return {"summary": summary[0]['summary_text']}
+
+@app.post("/personalized_simulation")
+def personalized_simulation(text: str):
+    inputs = personalized_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = personalized_model(**inputs)
+    logits = outputs.logits.detach().numpy()
+    pred_label = logits.argmax()
+    return {"predicted_label": int(pred_label)}
+