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from flask import Flask, render_template, request, jsonify
import google.generativeai as genai
import os
import tempfile
import base64
import logging
from dotenv import load_dotenv
# Configure logging
logging.basicConfig(level=logging.INFO)
# Load environment variables
load_dotenv()
app = Flask(__name__)
genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
@app.route("/")
def home():
return render_template("index.html")
@app.route("/process", methods=["POST"])
def process_image():
try:
data = request.json
image_data = data.get("image")
object_type = data.get("objectType", "").strip()
# Validate inputs
if not image_data or not object_type:
return jsonify({"success": False, "message": "Missing required parameters"})
# Decode image
try:
header, encoded = image_data.split(",", 1)
image_bytes = base64.b64decode(encoded)
except Exception as e:
logging.error(f"Image decoding failed: {str(e)}")
return jsonify({"success": False, "message": "Invalid image data"})
# Temporary files
with tempfile.TemporaryDirectory() as temp_dir:
input_path = os.path.join(temp_dir, "input.png")
with open(input_path, "wb") as f:
f.write(image_bytes)
# Configure model with safety settings
model = genai.GenerativeModel('gemini-2.0-flash-exp-image-generation')
prompt = f"Remove {object_type} from image naturally without text or artifacts"
response = model.generate_content(
[prompt, genai.upload_file(input_path)],
generation_config={
"temperature": 0.9,
"top_p": 0.95,
"top_k": 32,
"max_output_tokens": 4096,
},
safety_settings={
"HARM_CATEGORY_CIVIC_INTEGRITY": "BLOCK_ONLY_HIGH",
"HARM_CATEGORY_HARASSMENT": "BLOCK_NONE",
"HARM_CATEGORY_HATE_SPEECH": "BLOCK_NONE",
"HARM_CATEGORY_SEXUALLY_EXPLICIT": "BLOCK_NONE",
"HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_NONE"
}
)
# Process response
output_path = os.path.join(temp_dir, "result.png")
for chunk in response:
if chunk.candidates:
for part in chunk.candidates[0].content.parts:
if hasattr(part, 'inline_data'):
with open(output_path, "wb") as f:
f.write(part.inline_data.data)
return jsonify({
"success": True,
"resultPath": output_path
})
elif hasattr(part, 'text'):
logging.info(f"Text response: {part.text}")
return jsonify({"success": False, "message": "No valid output generated"})
except Exception as e:
logging.error(f"Processing error: {str(e)}")
return jsonify({"success": False, "message": f"Processing error: {str(e)}"})
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860) |