Update app.py

app.py

@@ -1,3 +1,66 @@
+import gradio as gr
+from random import randint
+from all_models import models
+from externalmod import gr_Interface_load
+import asyncio
+import os
+from threading import RLock
+from gradio_client import Client
+from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes
+
+lock = RLock()
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+def load_fn(models):
+    global models_load
+    models_load = {}
+    
+    for model in models:
+        if model not in models_load.keys():
+            try:
+                m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
+            except Exception as error:
+                print(error)
+                m = gr.Interface(lambda: None, ['text'], ['image'])
+            models_load.update({model: m})
+
+load_fn(models)
+
+num_models = 6
+MAX_SEED = 3999999999
+default_models = models[:num_models]
+inference_timeout = 600
+
+async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
+    kwargs = {"seed": seed}
+    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, **kwargs, token=HF_TOKEN))
+    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: {model_str}")
+        if not task.done(): 
+            task.cancel()
+        result = None
+    if task.done() and result is not None:
+        with lock:
+            png_path = "image.png"
+            result.save(png_path)
+        return png_path
+    return None
+
+# Expose Gradio API
+def generate_api(model_str, prompt, seed=1):
+    result = asyncio.run(infer(model_str, prompt, seed))
+    if result:
+        return result  # Path to generated image
+    return None
+
 @app.route('/predict', methods=['POST'])
 def predict():
     try:
@@ -41,4 +104,9 @@ def predict():
 
     except Exception as e:
         print(f"Error in /predict: {str(e)}")
-        return jsonify({"error": "An error occurred during processing."}), 500
+        return jsonify({"error": "An error occurred during processing."}), 500
+
+
+# Launch Gradio API without frontend
+iface = gr.Interface(fn=generate_api, inputs=["text", "text", "number"], outputs="file")
+iface.launch(show_api=True, share=True)