added AI layer for summarization and report generation

backend/app/db/database.py

@@ -17,4 +17,25 @@ def get_db():
     try:
         yield db
     finally:
-        db.close()
+        db.close()
+
+# am I supposed to use 'db' or 'base' or what so as to connect with supabase here?
+# backend/db.py
+from supabase import create_client
+
+SUPABASE_URL = os.getenv("SUPABASE_URL")
+SUPABASE_KEY = os.getenv("SUPABASE_KEY")
+
+supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+def get_impact_stats():
+    """
+    Fetch NGO impact stats from Supabase.
+    Expected table: 'impact_stats'
+    Example row: { totalLearners, supportPartners, volunteerHours, activeVolunteers }
+    """
+    response = supabase.table("impact_stats").select("*").execute() # so i can know what to use here ..................
+
+    if response.data and len(response.data) > 0:
+        return response.data[0]  # take first row for demo
+    return {"learners_reached": 0, "volunteer_hours": 0, "events_hosted": 0}

backend/app/db/seeds/volunteers.py

@@ -43,6 +43,6 @@ def seed_volunteers():
                 print(f"Adding Volunteer: {data['name']}")
 
         db.commit()
-        print("✅ Volunteers table seeded successfully.")
+        print("Volunteers table seeded successfully.")
     finally:
         db.close()

backend/main.py

@@ -1,12 +1,15 @@
-# main.py
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
 from app.db.database import Base, engine
 from app.api.routes.metrics import router as metrics_router
 from app.api.routes.impact import router as impact_router
 
-import app.api.models  
+import app.api.models 
+
+from summarizer import summarize_impact
+from db import get_impact_stats  # added this in database.py
+
 
 
 # Base.metadata.drop_all(bind=engine)
@@ -31,5 +34,19 @@
 def health_check():
     return {"message": f"Server is running and healthy"}
 
+# check here .....................
+@app.get("/report")
+def generate_report():
+    # Fetch stats from Supabase
+    stats = get_impact_stats()
+
+    # Pass stats to Gemini summarizer
+    summary = summarize_impact(stats)
+
+    return {
+        "stats": stats,
+        "summary": summary
+    }
+
 app.include_router(metrics_router, prefix="/api", tags=["metrics"])
-app.include_router(impact_router, prefix="/api", tags=["impact"])
+app.include_router(impact_router, prefix="/api", tags=["impact"])

backend/summarizer.py

@@ -0,0 +1,45 @@
+import os
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Configure Gemini client
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+
+# The summarizer function
+def summarize_impact(data: dict) -> str:
+    """
+    Summarize NGO impact stats into a donor-friendly narrative.
+
+    Args:
+        data (dict): Example:
+            {
+                "total_learners": 120,
+                "support_partners": 19,
+                "volunteer_hours": 85,
+                "active_volunteers": 30
+            }
+
+    Returns:
+        str: AI-generated summary text.
+    """
+    model = genai.GenerativeModel("gemini-1.5-flash")
+
+    prompt = f"""
+    You are helping an NGO write a donor-friendly impact report.
+
+    Stats provided:
+    - Total learners: {data.get('total_learners', 0)}
+    - Support partners: {data.get('support_partners', 0)}
+    - Volunteer hours: {data.get('volunteer_hours', 0)}
+    - Active volunteers: {data.get('active_volunteers', 0)}
+
+    Write a short, positive summary (3–4 sentences) highlighting achievements.
+    Make it clear, inspiring, and professional.
+    """
+
+    response = model.generate_content(prompt)
+
+    return response.text if response and response.text else "No summary generated."