Update app.py

app.py

@@ -1,64 +1,99 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+# File: app.py - FastAPI implementation for secure medical chatbot
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+import os
+import torch
+from fastapi import FastAPI, HTTPException, Depends, Security
+from fastapi.security import APIKeyHeader
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import hashlib
+import logging
 
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Initialize FastAPI app
+app = FastAPI(title="Secure Medical Chatbot API")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Setup CORS middleware to control which domains can access your API
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["https://your-website-domain.com"],  # Replace with your website domain
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-    messages.append({"role": "user", "content": message})
+# API key security
+API_KEY = os.environ.get("API_KEY", "your-secret-api-key")  # Set this securely in production
+api_key_header = APIKeyHeader(name="X-API-Key")
 
-    response = ""
+# Input model for request validation
+class QueryInput(BaseModel):
+    query: str
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# Create a hash function for privacy
+def hash_query(query: str) -> str:
+    return hashlib.sha256(query.encode()).hexdigest()
 
-        response += token
-        yield response
+# Load the model and tokenizer (lazy loading on first request)
+model = None
+tokenizer = None
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+def load_model():
+    global model, tokenizer
+    if model is None or tokenizer is None:
+        logger.info("Loading model and tokenizer...")
+        model_name = "shanover/medbot_godel_v3"
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model.to(device)
+        logger.info(f"Model loaded on {device}")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# Authentication dependency
+async def verify_api_key(api_key: str = Security(api_key_header)):
+    if api_key != API_KEY:
+        raise HTTPException(status_code=403, detail="Invalid API key")
+    return api_key
+
+# Generate response function
+def generate_response(input_text, max_length=512):
+    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=max_length, truncation=True)
+    input_ids = input_ids.to(device)
+    with torch.no_grad():
+        output_ids = model.generate(input_ids)
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return generated_text
+
+@app.on_event("startup")
+async def startup_event():
+    load_model()
+
+@app.post("/api/medical-advice")
+async def get_medical_advice(query_input: QueryInput, api_key: str = Depends(verify_api_key)):
+    try:
+        query = query_input.query
+        # Log a hash of the query instead of the query itself for privacy
+        logger.info(f"Processing query with hash: {hash_query(query)}")
+        
+        response = generate_response(query)
+        
+        return {
+            "response": response,
+            "status": "success"
+        }
+    except Exception as e:
+        logger.error(f"Error processing query: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error processing request: {str(e)}")
 
+# Health check endpoint
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
 
 if __name__ == "__main__":
-    demo.launch()
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)