Update app.py

app.py

@@ -1,87 +1,70 @@
-import os
-import gradio as gr
-from helper import generate_reply
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Tuple, Dict
+import helper
+import dialogue
+import models.linear as linear_model
+import models.logistic as logistic_model
+import judge
 
-# Ensure your API key is set as an env var (no dotenv needed on HF Spaces)
-OPENAI_KEY = os.getenv("OPENAI_API_KEY")
-if not OPENAI_KEY:
-    raise ValueError("🔑 Set OPENAI_API_KEY in your Space’s Secrets.")
+app = FastAPI(
+    title="KindCare Chat API with ML",
+    description="Chat + prediction + judge endpoints"
+)
 
-# Response function
-def respond(user_message, history):
-    bot_message = generate_reply(user_message)
-    history = history or []
-    history.append((user_message, bot_message))
-    return history, ""
+class ChatRequest(BaseModel):
+    message: str
+    history: List[Tuple[str, str]] = []
 
-# Custom CSS for appearance tweaks
-css = """
-body {
-    background: #121212;
-    color: #e0e0e0;
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-}
-.gradio-container {
-    max-width: 700px;
-    margin: auto;
-    padding: 1.5rem;
-    backdrop-filter: blur(10px);
-    background-color: rgba(30, 30, 46, 0.85);
-    border-radius: 16px;
-    box-shadow: 0 8px 20px rgba(0,0,0,0.5);
-}
-.gradio-container h1, .gradio-container h2, .gradio-container h3 {
-    color: #ffffff;
-    text-shadow: 0px 2px 4px rgba(0,0,0,0.6);
-}
-/* Chat bubbles */
-.chatbot .chat-message.user {
-    background: linear-gradient(120deg, #0066ff, #3399ff) !important;
-    color: white !important;
-    border-radius: 16px !important;
-    padding: 0.6rem 1rem !important;
-}
-.chatbot .chat-message.assistant {
-    background: #262635 !important;
-    color: #e0e0e0 !important;
-    border-radius: 16px !important;
-    padding: 0.6rem 1rem !important;
-}
-/* Textbox and button tweaks */
-#textbox textarea {
-    background-color: #1e1e2e !important;
-    border: 1px solid #333 !important;
-    border-radius: 8px !important;
-}
-#textbox textarea:focus {
-    border-color: #0066ff !important;
-}
-#send-btn {
-    background-color: #0066cc !important;
-    border-radius: 8px !important;
-    padding: 0.6rem 1.2rem !important;
-    color: white !important;
-    font-weight: bold !important;
-}
-#send-btn:hover {
-    background-color: #005bb5 !important;
-}
-"""
+class ChatResponse(BaseModel):
+    reply: str
+    history: List[Tuple[str, str]]
 
-# Build UI
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("# 🤖 AI Companion\n*Warm, friendly chat with your bot*")
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    with gr.Row():
-        txt = gr.Textbox(
-            placeholder="Type your message here…", 
-            show_label=False, 
-            lines=1,
-            elem_id="textbox"
-        )
-        send = gr.Button("Send", elem_id="send-btn")
-    txt.submit(respond, [txt, chatbot], [chatbot, txt])
-    send.click(respond, [txt, chatbot], [chatbot, txt])
+class PredictRequest(BaseModel):
+    features: Dict[str, float]
+
+class PredictLinearResponse(BaseModel):
+    prediction: float
+    metrics: Dict[str, float]
+
+class PredictLogisticRequest(PredictRequest):
+    bins: List[str]
+
+class PredictLogisticResponse(BaseModel):
+    prediction: str
+    metrics: Dict[str, float]
+
+class JudgeRequest(BaseModel):
+    model_type: str
+    metrics: Dict[str, float]
+
+class JudgeResponse(BaseModel):
+    verdict: str
+    comments: List[str]
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(req: ChatRequest):
+    try:
+        reply, hist = dialogue.handle_message(req.message, req.history)
+        return ChatResponse(reply=reply, history=hist)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/predict/linear", response_model=PredictLinearResponse)
+async def predict_linear(req: PredictRequest):
+    pred, metrics = linear_model.predict(req.features)
+    return PredictLinearResponse(prediction=pred, metrics=metrics)
+
+@app.post("/predict/logistic", response_model=PredictLogisticResponse)
+async def predict_logistic(req: PredictLogisticRequest):
+    pred, metrics = logistic_model.predict(req.features, req.bins)
+    return PredictLogisticResponse(prediction=pred, metrics=metrics)
+
+@app.post("/judge/model", response_model=JudgeResponse)
+async def judge_endpoint(req: JudgeRequest):
+    result = judge.judge_model(req.model_type, req.metrics)
+    return JudgeResponse(**result)
 
 if __name__ == "__main__":
-    demo.launch()
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=8000)