Update app.py

app.py

@@ -1,44 +1,60 @@
-from flask import Flask, request, jsonify, send_file
-from gradio_client import Client
+import gradio as gr
+from random import randint
+from all_models import models
+from externalmod import gr_Interface_load
+import asyncio
 import os
-from all_models import models  # Import your models
-
-app = Flask(__name__)
-
-# Initialize the Gradio client
-client = Client("Geek7/mdztxi2")
-
-@app.route('/generate-image', methods=['POST'])
-def generate_image():
-    data = request.get_json()
-
-    # Check for required fields in the request data
-    model_str = data.get('model_str')
-    prompt = data.get('prompt')
-    seed = data.get('seed')
-
-    if not model_str or not prompt:
-        return jsonify({"error": "Model string and prompt are required."}), 400
-
-    # Make a prediction request
+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:
-        # Making sure to capture the actual result returned
-        result = client.predict(
-            model_str=model_str,
-            prompt=prompt,
-            seed=seed,
-            api_name="/predict"
-        )
-
-        # If the result is a file path, send it; if it's image bytes, handle accordingly
-        if isinstance(result, str) and os.path.exists(result):  # If the result is a path
-            return send_file(result, mimetype='image/png')  # Send the generated image file
-        elif isinstance(result, bytes):  # If the result is in bytes
-            return send_file(io.BytesIO(result), mimetype='image/png')  # Return image bytes as a file
-        else:
-            return jsonify({"error": "Image generation failed, result not valid."}), 500
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == '__main__':
-    app.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)