app.py

from fastapi import FastAPI, Request
import requests
import os
import json

app = FastAPI()

GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
GEMINI_ENDPOINT = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={GEMINI_API_KEY}"


@app.get("/")
async def root():
    return {"message": "AI Insight API is running. Use POST /api/ai-insight"}


@app.post("/api/ai-insight")
async def generate_insight(request: Request):
    body = await request.json()
    prompt_type = body.get("type", "general")
    data = body.get("data", [])
    base_prompt = ""

    if prompt_type == "compliance":
        base_prompt = f"""
        You are a compliance analyst.

        Analyze the following compliance document status data and return a JSON with:
        {{
          "summary": "...",
          "risks": ["..."],
          "recommendations": ["..."]
        }}

        Data:
        {json.dumps(data, indent=2)}
        """
    elif prompt_type == "resources":
        base_prompt = f"""
        You are a resource utilization expert.

        Analyze the resource usage data below. Return a JSON:
        {{
          "summary": "...",
          "overutilized": ["..."],
          "underutilized": ["..."],
          "suggestions": ["..."]
        }}

        Data:
        {json.dumps(data, indent=2)}
        """
    elif prompt_type == "participants":
        base_prompt = f"""
        You are a program analyst.

        Analyze participant growth metrics and return JSON like:
        {{
          "summary": "...",
          "trends": ["..."],
          "risks": ["..."],
          "recommendations": ["..."]
        }}

        Data:
        {json.dumps(data, indent=2)}
        """
    elif prompt_type == "intervention":
        base_prompt = f"""
        You are an incubation operations strategist.

        Analyze intervention frequency, completion and category data. Return a JSON:
        {{
          "summary": "...",
          "effective_areas": ["..."],
          "gaps": ["..."],
          "recommendations": ["..."]
        }}

        Data:
        {json.dumps(data, indent=2)}
        """
    elif prompt_type == "consultant":
        base_prompt = f"""
        You are evaluating consultant performance for an incubator program.

        Using metrics like hours, rating, and impact scores, return JSON like:
        {{
          "summary": "...",
          "top_performers": ["..."],
          "underperformers": ["..."],
          "recommendations": ["..."]
        }}

        Data:
        {json.dumps(data, indent=2)}
        """
    else:
        base_prompt = f"""
        Analyze the following data and return a useful JSON summary:
        {{
          "summary": "...",
          "details": ["..."],
          "recommendations": ["..."]
        }}

        Data:
        {json.dumps(data, indent=2)}
        """

    response = requests.post(
        GEMINI_ENDPOINT,
        headers={"Content-Type": "application/json"},
        json={"contents": [{"parts": [{"text": base_prompt}]}]},
    )

    try:
        result = response.json()
        insight_text = result["candidates"][0]["content"]["parts"][0]["text"]
        return {
            "insight": insight_text
        }
    except Exception:
        return {
            "error": "Gemini response format invalid",
            "details": str(response.json())
        }