Update myapp.py

myapp.py

@@ -1,77 +1,45 @@
 from flask import Flask, request, jsonify, send_file
-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 PIL import Image
+from flask_cors import CORS
+from gradio_client import Client
+from all_models import models  # Import the models list
 
-myapp = Flask(__name__)
+app = Flask(__name__)
+CORS(app)
 
-lock = RLock()
-HF_TOKEN = os.environ.get("HF_TOKEN")
+# Initialize Gradio Client with the first model in the list
+client = Client("Geek7/mdztxi2")
 
-# Load models
-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
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.get_json()
 
-# Gradio inference function
-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 = "generated_image.png"
-            result.save(png_path)  # Save the result as an image
-        return png_path
-    return None
+    # 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
 
-# API function to perform inference
-@myapp.route('/generate-image', methods=['POST'])
-def generate_image():
-    data = request.get_json()
     model_str = data['model_str']
     prompt = data['prompt']
-    seed = data.get('seed', 1)
+    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
 
-    # Run Gradio inference
-    result_path = asyncio.run(infer(model_str, prompt, seed))
-    
-    if result_path:
-        # Send back the generated image file
+    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')
-    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__":
-    myapp.run(host='0.0.0.0', port=7860)  # Run directly
+if __name__ == '__main__':
+    app.run(debug=True)