Update app.py

app.py

@@ -1,119 +1,163 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, Field
-from typing import Annotated, Literal
-from prediction_helper import predict
-from one_shot_bot import generate_advice
-from chatbot_advisor import ask_chatbot
-
-class ModelInput(BaseModel):
-    gender: Annotated[Literal["male", "female"], Field(description="Enter your gender")]
-    marital_status: Annotated[Literal["married", "unmarried"], Field(description="Enter your marital status")]
-    age: Annotated[int, Field(gt=0, lt=110, description="Enter your age")]
-    number_of_dependants: Annotated[int, Field(gt=0, lt=8)]
-    income_lakhs: Annotated[float, Field(gt=0,description="Enter your annual income in lakhs")]
-    genetical_risk: Annotated[int, Field(gt=0, lt=6)]
-    insurance_plan: Annotated[Literal['Bronze', 'Silver', 'Gold'], Field(description="Choose one of the given plans")]
-    employment_status: Annotated[Literal['Salaried', 'Self-Employed', 'Freelancer'], Field()]
-    bmi_category: Annotated[Literal['Normal', 'Obesity', 'Overweight', 'Underweight'], Field()]
-    smoking_status: Annotated[Literal['No Smoking', 'Regular', 'Occasional'], Field()]
-    region: Annotated[Literal['Northwest', 'Southeast', 'Northeast', 'Southwest'], Field()]
-    medical_history: Annotated[
-        Literal[
-            'No Disease', 'Diabetes', 'High blood pressure', 'Diabetes & High blood pressure',
-            'Thyroid', 'Heart disease', 'High blood pressure & Heart disease',
-            'Diabetes & Thyroid', 'Diabetes & Heart disease'
-        ],
-        Field()
-    ]
-
-class ModelOutput(BaseModel):
-    yearly : float
-    monthly : float
-    advice : str
-
-class ChatMessage(BaseModel):
-    thread_id : str
-    message : str
-    yearly_cost : float
-    monthly_cost : float
-    ai_summary : str
-
-app = FastAPI()
-
-@app.get("/plans")
-def plans():
-    return {
-        "Bronze": "Basic coverage, low premium.",
-        "Silver": "Balance of premium and coverage.",
-        "Gold": "Premium cost with highest benefits."
-    }
-
-
-@app.get("/home")
-def home():
-    return {"message": "Welcome! The API is live"}
-
-@app.post("/predict", response_model=ModelOutput)
-def predict_output(input_data: ModelInput):
-    try:
-        data = input_data.model_dump()
-
-        converted_data = {
-            "Gender": data["gender"].title(),
-            "Marital Status": data["marital_status"].title(),
-            "Age": data["age"],
-            "Number of Dependants": data["number_of_dependants"],
-            "Income in Lakhs": data["income_lakhs"],
-            "Genetical Risk": data["genetical_risk"],
-            "Insurance Plan": data["insurance_plan"].title(),
-            "Employment Status": data["employment_status"],
-            "BMI Category": data["bmi_category"],
-            "Smoking Status": data["smoking_status"],
-            "Region": data["region"],
-            "Medical History": data["medical_history"]
-        }
-
-        yearly_prediction = float(predict(converted_data))
-        monthly = round(yearly_prediction / 12, 2)
-
-        advice = generate_advice(
-            yearly_premium=yearly_prediction,
-            monthly_premium=monthly,
-            age=input_data.age,
-            gender=input_data.gender,
-            marital_status=input_data.marital_status,
-            dependents=input_data.number_of_dependants,
-            bmi_category=input_data.bmi_category,
-            smoking_status=input_data.smoking_status,
-            medical_history=input_data.medical_history,
-            genetic_risk=input_data.genetical_risk,
-            region=input_data.region,
-            income_lakhs=input_data.income_lakhs,
-            employment_status=input_data.employment_status,
-            insurance_plan=input_data.insurance_plan
-        )
-
-        return ModelOutput(yearly=yearly_prediction, monthly=monthly, advice=advice)
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Prediction Failed: {str(e)}")
-
-
-@app.post('/chat')
-def chat(input_data : ChatMessage):
-    try:
-        yearly_cost = input_data.yearly_cost
-        monthly_cost = input_data.monthly_cost
-        ai_summary = input_data.ai_summary
-
-        response = ask_chatbot(
-            yearly_cost=yearly_cost,
-            monthly_cost=monthly_cost,
-            ai_summary=ai_summary,
-            user_message=input_data.message,
-            thread_id=input_data.thread_id
-        )
-        return {"response": response}
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+from fastapi import FastAPI, HTTPException, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse, StreamingResponse
+from pydantic import BaseModel
+import os
+from one_shot_bot import generate_advice
+from prediction_helper import predict
+from chatbot_advisor import ask_chatbot
+from utility import STT, TTS
+
+app = FastAPI()
+
+# ---------------- CORS ---------------- #
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ---------------- MODELS ---------------- #
+
+class HealthInput(BaseModel):
+    gender: str
+    marital_status: str
+    age: int
+    number_of_dependants: int
+    income_lakhs: float
+    genetical_risk: int
+    insurance_plan: str
+    employment_status: str
+    bmi_category: str
+    smoking_status: str
+    region: str
+    medical_history: str
+
+
+class HealthOutput(BaseModel):
+    yearly: float
+    monthly: float
+    advice: str
+
+
+class ChatMessage(BaseModel):
+    thread_id: str
+    message: str
+    yearly_cost: float
+    monthly_cost: float
+    ai_summary: str
+
+
+class TTSRequest(BaseModel):
+    text: str
+
+
+# ---------------- BASIC ROUTE ---------------- #
+
+@app.get("/")
+def home():
+    return {"message": "Healthcare AI API is running."}
+
+
+# ---------------- PREDICTION ---------------- #
+
+@app.post("/predict", response_model=HealthOutput)
+def predict_output(input_data: HealthInput):
+    try:
+        data = input_data.model_dump()
+
+        converted_data = {
+            "Gender": data["gender"].title(),
+            "Marital Status": data["marital_status"].title(),
+            "Age": data["age"],
+            "Number of Dependants": data["number_of_dependants"],
+            "Income in Lakhs": data["income_lakhs"],
+            "Genetical Risk": data["genetical_risk"],
+            "Insurance Plan": data["insurance_plan"].title(),
+            "Employment Status": data["employment_status"],
+            "BMI Category": data["bmi_category"],
+            "Smoking Status": data["smoking_status"],
+            "Region": data["region"],
+            "Medical History": data["medical_history"]
+        }
+
+        yearly_prediction = float(predict(converted_data))
+        monthly = round(yearly_prediction / 12, 2)
+
+        advice = generate_advice(
+            yearly_premium=yearly_prediction,
+            monthly_premium=monthly,
+            age=input_data.age,
+            gender=input_data.gender,
+            marital_status=input_data.marital_status,
+            dependents=input_data.number_of_dependants,
+            bmi_category=input_data.bmi_category,
+            smoking_status=input_data.smoking_status,
+            medical_history=input_data.medical_history,
+            genetic_risk=input_data.genetical_risk,
+            region=input_data.region,
+            income_lakhs=input_data.income_lakhs,
+            employment_status=input_data.employment_status,
+            insurance_plan=input_data.insurance_plan
+        )
+
+        return HealthOutput(yearly=yearly_prediction, monthly=monthly, advice=advice)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction Failed: {str(e)}")
+
+# ---------------- CHAT STREAM ---------------- #
+
+@app.post('/chat')
+def chat(input_data : ChatMessage):
+    try:
+        yearly_cost = input_data.yearly_cost
+        monthly_cost = input_data.monthly_cost
+        ai_summary = input_data.ai_summary
+
+        response = ask_chatbot(
+            yearly_cost=yearly_cost,
+            monthly_cost=monthly_cost,
+            ai_summary=ai_summary,
+            user_message=input_data.message,
+            thread_id=input_data.thread_id
+        )
+        return response
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ---------------- TTS ---------------- #
+
+@app.post("/tts")
+async def generate_tts(request: TTSRequest):
+    try:
+        if not request.text.strip():
+            raise HTTPException(status_code=400, detail="Text is empty")
+
+        audio_path = await TTS(text=request.text)
+
+        if not os.path.exists(audio_path):
+            raise HTTPException(status_code=500, detail="Audio file not created")
+
+        return FileResponse(
+            path=audio_path,
+            media_type="audio/mpeg",
+            filename="speech.mp3"
+        )
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ---------------- STT ---------------- #
+
+@app.post("/stt")
+async def transcribe_audio(file: UploadFile = File(...)):
+    try:
+        return await STT(file)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))