Update myapp.py

myapp.py

@@ -1,16 +1,81 @@
-from flask import Flask, jsonify, request, send_file
+from flask import Flask, request, jsonify, send_file
 from flask_cors import CORS
-import io
+import asyncio
+import tempfile
+import os
+from threading import RLock
+from huggingface_hub import InferenceClient
+from all_models import models  # Importing models from all_models
 
-# Initialize the Flask app
 myapp = Flask(__name__)
-CORS(myapp)  # Enable CORS if needed
+CORS(myapp)  # Enable CORS for all routes
+
+lock = RLock()
+HF_TOKEN = os.environ.get("HF_TOKEN")  # Hugging Face token
+
+inference_timeout = 600  # Set timeout for inference
 
 @myapp.route('/')
 def home():
-    return "Welcome to the Image Background Remover!"  # Basic home response
+    return "Welcome to the Image Background Remover!"
+
+# Function to dynamically load models from the "models" list
+def get_model_from_name(model_name):
+    return model_name if model_name in models else None
+
+# Asynchronous function to perform inference
+async def infer(client, prompt, seed=1, timeout=inference_timeout, model="prompthero/openjourney-v4"):
+    task = asyncio.create_task(
+        asyncio.to_thread(client.text_to_image, prompt=prompt, seed=seed, model=model)
+    )
+    await asyncio.sleep(0)
+    try:
+        result = await asyncio.wait_for(task, timeout=timeout)
+    except (Exception, asyncio.TimeoutError) as e:
+        print(e)
+        print(f"Task timed out for model: {model}")
+        if not task.done():
+            task.cancel()
+        result = None
+    
+    if task.done() and result is not None:
+        with lock:
+            temp_image = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
+            with open(temp_image.name, "wb") as f:
+                f.write(result)  # Save the result image as a temporary file
+        return temp_image.name  # Return the path to the saved image
+    return None
+
+# Flask route for the API endpoint
+@myapp.route('/generate_api', methods=['POST'])
+def generate_api():
+    data = request.get_json()
+
+    # Extract required fields from the request
+    prompt = data.get('prompt', '')
+    seed = data.get('seed', 1)
+    model_name = data.get('model', 'prompthero/openjourney-v4')  # Default to "prompthero/openjourney-v4" if not provided
+
+    if not prompt:
+        return jsonify({"error": "Prompt is required"}), 400
+
+    # Get the model from all_models
+    model = get_model_from_name(model_name)
+    if not model:
+        return jsonify({"error": f"Model '{model_name}' not found in available models"}), 400
 
+    try:
+        # Create a generic InferenceClient for the model
+        client = InferenceClient(token=HF_TOKEN)  # Pass Hugging Face token if needed
 
+        # Call the async inference function
+        result_path = asyncio.run(infer(client, prompt, seed, model=model))
+        if result_path:
+            return send_file(result_path, mimetype='image/png')  # Send back the generated image file
+        else:
+            return jsonify({"error": "Failed to generate image"}), 500
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
 
 # Add this block to make sure your app runs when called
 if __name__ == "__main__":