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scripts/generate-fidya-images.py
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208
scripts/generate-fidya-images.py
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#!/usr/bin/env python3
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"""
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Generate on-brand photography for the Charity Right Fidya/Kaffarah page
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using Gemini Nano Banana Pro (Gemini 3 Pro Image Preview).
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Brand style reference (from Ramadan homepage):
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- Documentary/photojournalistic style
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- Warm earth tones (sandy, ochre, warm brown palette)
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- Natural golden hour lighting
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- Shallow depth of field (subject sharp, background softly blurred)
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- Dignified portraits of people in need (children, elderly, families)
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- High quality, professional look
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- Wide aspect ratio (~16:9)
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"""
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import os
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import json
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import base64
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import sys
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import time
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from pathlib import Path
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# Load from .env
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from dotenv import load_dotenv
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load_dotenv()
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API_KEY = os.getenv("GEMINI_API_KEY")
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MODEL = os.getenv("GEMINI_IMAGE_MODEL", "nano-banana-pro-preview")
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if not API_KEY:
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print("ERROR: GEMINI_API_KEY not set in .env")
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sys.exit(1)
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print(f"Using model: {MODEL}")
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print(f"API Key: {API_KEY[:10]}...")
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import requests
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OUTPUT_DIR = Path("screenshots/fidya-kaffarah")
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# Image prompts designed to match the CR brand photography style for fidya/kaffarah context
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PROMPTS = [
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{
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"name": "fidya-hero",
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"prompt": (
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"Documentary photography, wide shot. An elderly South Asian grandmother "
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"sitting peacefully in a modest home, warm golden hour light streaming through "
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"a window, she has gentle wrinkles and wise eyes, wearing a simple white cotton "
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"dupatta over her head. A plate of simple food (rice and lentils) sits beside her. "
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"Warm earth tones, shallow depth of field, photojournalistic style, "
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"shot on 85mm lens, natural lighting. Dignified and compassionate mood. "
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"16:9 aspect ratio, high resolution professional photograph."
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)
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},
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{
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"name": "fidya-feeding",
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"prompt": (
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"Documentary photography. A warm scene of food being served to people in need "
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"at a community feeding program in rural Bangladesh. Focus on hands gently "
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"placing a plate of rice and curry in front of a grateful elderly person. "
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"Golden warm lighting, earth tones, shallow depth of field with other people "
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"softly blurred in background. Photojournalistic style, authentic, dignified. "
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"Shot on 50mm lens, natural light. 16:9 aspect ratio, professional photograph."
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)
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},
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{
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"name": "kaffarah-community",
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"prompt": (
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"Documentary photography. A community meal distribution scene in a rural village. "
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"Several children and elderly people receiving meals from volunteers. "
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"The scene is outdoors with dusty earth ground and simple structures in background. "
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"Warm golden hour sunlight, rich earth tones (ochre, sand, brown). "
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"Focus on a child holding a bowl of food, looking up with hopeful eyes. "
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"Shallow depth of field, photojournalistic authenticity. "
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"Shot on 35mm lens. 16:9 aspect ratio, professional photograph."
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)
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},
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{
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"name": "fidya-quran-elder",
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"prompt": (
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"Documentary photography, intimate portrait. An elderly Muslim man with a white beard "
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"and simple prayer cap, sitting cross-legged on the floor in a humble room, "
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"holding prayer beads (tasbih). Soft warm natural light from a side window "
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"illuminating his weathered, peaceful face. Warm earth tone palette, "
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"shallow depth of field, background is a simple mud-colored wall. "
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"Dignified, contemplative, spiritual mood. Shot on 85mm lens. "
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"16:9 aspect ratio, high resolution professional photograph."
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)
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},
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{
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"name": "fidya-child-meal",
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"prompt": (
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"Documentary photography. A young child in South Asia or East Africa, "
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"around 6-8 years old, sitting at a simple wooden table eating a plate of food "
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"(rice and vegetables). The child has a subtle, content smile. "
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"Warm golden natural light, earth tones (sandy, ochre), background is softly "
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"blurred showing a simple classroom or community center. "
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"Shallow depth of field, photojournalistic style. Dignified, hopeful. "
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"Shot on 50mm lens. 16:9 aspect ratio, professional photograph."
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)
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},
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]
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def generate_image(prompt_data):
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"""Generate an image using Gemini Nano Banana Pro."""
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name = prompt_data["name"]
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prompt = prompt_data["prompt"]
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url = f"https://generativelanguage.googleapis.com/v1beta/models/{MODEL}:generateContent?key={API_KEY}"
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payload = {
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"contents": [
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{
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"parts": [
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{"text": prompt}
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]
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}
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],
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"generationConfig": {
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"responseModalities": ["TEXT", "IMAGE"],
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"temperature": 1.0,
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}
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}
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headers = {"Content-Type": "application/json"}
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print(f"\n[*] Generating: {name}")
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print(f" Prompt: {prompt[:80]}...")
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try:
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response = requests.post(url, json=payload, headers=headers, timeout=120)
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if response.status_code != 200:
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print(f" [ERR] Error {response.status_code}: {response.text[:300]}")
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return None
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data = response.json()
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# Extract image from response
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candidates = data.get("candidates", [])
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if not candidates:
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print(f" [ERR] No candidates in response")
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print(f" Response: {json.dumps(data, indent=2)[:500]}")
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return None
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parts = candidates[0].get("content", {}).get("parts", [])
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for part in parts:
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if "inlineData" in part:
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image_data = part["inlineData"]
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mime_type = image_data.get("mimeType", "image/png")
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b64_data = image_data["data"]
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# Determine extension
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ext = "png" if "png" in mime_type else "jpg"
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output_path = OUTPUT_DIR / f"{name}.{ext}"
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# Decode and save
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img_bytes = base64.b64decode(b64_data)
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output_path.write_bytes(img_bytes)
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size_kb = len(img_bytes) / 1024
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print(f" [OK] Saved: {output_path} ({size_kb:.0f} KB)")
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return str(output_path)
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# If we get here, no image was found
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print(f" [ERR] No image in response parts")
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for part in parts:
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if "text" in part:
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print(f" Text response: {part['text'][:200]}")
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return None
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except Exception as e:
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print(f" [ERR] Exception: {e}")
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return None
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def main():
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print("=" * 60)
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print("Charity Right — Fidya/Kaffarah Photography Generator")
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print(f"Model: {MODEL}")
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print("=" * 60)
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results = []
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for i, prompt_data in enumerate(PROMPTS):
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result = generate_image(prompt_data)
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results.append((prompt_data["name"], result))
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# Small delay between requests to avoid rate limiting
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if i < len(PROMPTS) - 1:
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print(" ... Waiting 3s before next generation...")
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time.sleep(3)
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print("\n" + "=" * 60)
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print("RESULTS SUMMARY")
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print("=" * 60)
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for name, path in results:
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status = f"[OK] {path}" if path else "[FAIL] Failed"
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print(f" {name}: {status}")
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successful = sum(1 for _, p in results if p)
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print(f"\n {successful}/{len(results)} images generated successfully")
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if __name__ == "__main__":
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main()
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