Update myapp.py

myapp.py

@@ -1,45 +1,60 @@
-from flask import Flask, request, jsonify, send_file
-from flask_cors import CORS
-from gradio_client import Client
-from all_models import models  # Import the models list
-
-myapp = Flask(__name__)
-CORS(myapp)
-
-# Initialize Gradio Client with the first model in the list
-client = Client("Geek7/mdztxi2")
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    data = request.get_json()
-
-    # Validate required fields
-    if not data or 'model_str' not in data or 'prompt' not in data or 'seed' not in data:
-        return jsonify({"error": "Missing required fields"}), 400
-
-    model_str = data['model_str']
-    prompt = data['prompt']
-    seed = data['seed']
-
-    # Check if the model_str exists in the models list
-    if model_str not in models:
-        return jsonify({"error": f"Model '{model_str}' is not available."}), 400
-
+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
+
+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:
-        # Send a request to the Gradio Client and get the result
-        result = client.predict(
-            model_str=model_str,
-            prompt=prompt,
-            seed=seed,
-            api_name="/predict"
-        )
-        
-        # Save the result to a file (assuming it returns a filepath)
-        result_path = result  # Result is already the filepath
-        return send_file(result_path, mimetype='image/png')
-
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == '__main__':
-    myapp.run(debug=True)
+        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
+
+# Launch Gradio API without frontend
+iface = gr.Interface(fn=generate_api, inputs=["text", "text", "number"], outputs="file")
+iface.launch(show_api=True, share=True)