app.py

import os
import base64
import mimetypes
from flask import Flask, render_template, request, jsonify
from werkzeug.utils import secure_filename
from google import genai
from google.genai import types

# Initialize Flask app
app = Flask(__name__)

# Read the Gemini API key from environment variables (set in Hugging Face Spaces)
GEMINI_API_KEY = os.environ["GEMINI_API_KEY"]
client = genai.Client(api_key=GEMINI_API_KEY)

# Create necessary directories
UPLOAD_FOLDER = 'uploads'
RESULT_FOLDER = os.path.join('static')
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(RESULT_FOLDER, exist_ok=True)

def generate_gemini_output(object_type, image_data_url):
    """
    Generate output from Gemini by removing the specified object.
    Expects the image_data_url to be a base64 data URL.
    """
    model = "gemini-2.0-flash-lite"  # Use the lite model for text-based responses
    files = []

    # Decode the image data from the data URL
    if image_data_url:
        try:
            header, encoded = image_data_url.split(',', 1)
        except ValueError:
            raise ValueError("Invalid image data")
        binary_data = base64.b64decode(encoded)
        # Determine file extension from header
        ext = ".png" if "png" in header.lower() else ".jpg"
        temp_filename = secure_filename("temp_image" + ext)
        temp_filepath = os.path.join(UPLOAD_FOLDER, temp_filename)
        with open(temp_filepath, "wb") as f:
            f.write(binary_data)
        # Upload file to Gemini
        uploaded_file = client.files.upload(file=temp_filepath)
        files.append(uploaded_file)

    # Prepare content parts for Gemini
    parts = []
    if files:
        parts.append(types.Part.from_uri(file_uri=files[0].uri, mime_type=files[0].mime_type))
    if object_type:
        # Gemini magic prompt: instruct the model to remove the specified object
        magic_prompt = f"Remove {object_type} from the image"
        parts.append(types.Part.from_text(text=magic_prompt))

    contents = [types.Content(role="user", parts=parts)]

    generate_content_config = types.GenerateContentConfig(
        temperature=1,
        top_p=0.95,
        top_k=40,
        max_output_tokens=8192,
        response_mime_type="text/plain",
        system_instruction=[
            types.Part.from_text(text="""Your AI finds user requests about removing objects from images.
If the user asks to remove a person or animal, respond with 'No'."""),
        ],
    )

    result_text = None

    # Stream output from Gemini API
    for chunk in client.models.generate_content_stream(
        model=model,
        contents=contents,
        config=generate_content_config,
    ):
        if not chunk.candidates or not chunk.candidates[0].content or not chunk.candidates[0].content.parts:
            continue
        part = chunk.candidates[0].content.parts[0]
        if part.text:
            result_text = part.text

    # If the response is "No", switch to the image generation model
    if result_text and "no" in result_text.lower():
        model = "gemini-2.0-flash-exp-image-generation"
        generate_content_config.response_modalities = ["image", "text"]
        for chunk in client.models.generate_content_stream(
            model=model,
            contents=contents,
            config=generate_content_config,
        ):
            if not chunk.candidates or not chunk.candidates[0].content or not chunk.candidates[0].content.parts:
                continue
            part = chunk.candidates[0].content.parts[0]
            if part.inline_data:
                file_extension = mimetypes.guess_extension(part.inline_data.mime_type) or ".png"
                output_filename = secure_filename("generated_output" + file_extension)
                result_image_path = os.path.join(RESULT_FOLDER, output_filename)
                with open(result_image_path, "wb") as f:
                    f.write(part.inline_data.data)
                result_image = result_image_path  # Path relative to static folder
                return result_text, result_image

    return result_text, None

@app.route("/")
def index():
    # Render the front-end HTML (which contains complete HTML/CSS/JS inline)
    return render_template("index.html")

@app.route("/process", methods=["POST"])
def process():
    try:
        # Expect JSON with keys "image" (base64 data URL) and "objectType"
        data = request.get_json(force=True)
        image_data = data.get("image")
        object_type = data.get("objectType", "").strip()
        if not image_data or not object_type:
            return jsonify({"success": False, "message": "Missing image data or object type."}), 400

        # Generate output using Gemini
        result_text, result_image = generate_gemini_output(object_type, image_data)
        if not result_image:
            return jsonify({"success": False, "message": result_text or "Failed to generate image."}), 500

        # Create a URL to serve the image from the static folder.
        image_url = f"/static/{os.path.basename(result_image)}"

        return jsonify({"success": True, "resultPath": image_url, "resultText": result_text})
    except Exception as e:
        return jsonify({"success": False, "message": f"Error: {str(e)}"}), 500

if __name__ == "__main__":
    # Run the app on port 5000 or the port provided by the environment (for Hugging Face Spaces)
    app.run(host="0.0.0.0", port=7860)