Update app.py

app.py

@@ -1,14 +1,21 @@
 import gradio as gr
 from random import randint
-from all_models import models
+from all_models import models  # Import the list of available models
 from externalmod import gr_Interface_load
 import asyncio
 import os
 from threading import RLock
+from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
+import tempfile
 
 lock = RLock()
 HF_TOKEN = os.environ.get("HF_TOKEN")
 
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes
+
+# Function to load models
 def load_fn(models):
     global models_load
     models_load = {}
@@ -24,11 +31,12 @@ def load_fn(models):
 
 load_fn(models)
 
-num_models = 6
+num_models = 6  # Number of models to load initially
 MAX_SEED = 3999999999
-default_models = models[:num_models]
+default_models = models[:num_models]  # Load the first few models for inference
 inference_timeout = 600
 
+# Asynchronous function to perform inference
 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))
@@ -43,30 +51,33 @@ async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
         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
+            temp_image = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
+            result.save(temp_image.name)  # Save result as a temporary file
+        return temp_image.name  # Return the path to the saved image
     return None
 
-from gradio_client import Client
-
-client = Client("Geek7/mdztxi2")
-result = client.predict(
-		model_str=model_str,
-		prompt=prompt,
-		seed=seed,
-		api_name="/generate_api "
-)
+# Flask route for the API endpoint
+@app.route('/generate_api', methods=['POST'])
+def generate_api():
+    data = request.get_json()
 
+    # Extract required fields from the request
+    model_str = data.get('model_str', default_models[0])  # Default to first model if not provided
+    prompt = data.get('prompt', '')
+    seed = data.get('seed', 1)
 
+    if not prompt:
+        return jsonify({"error": "Prompt is required"}), 400
+    
+    try:
+        # Call the async inference function
+        result_path = asyncio.run(infer(model_str, prompt, seed))
+        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
 
-# Launch Gradio API without frontend
-iface = gr.Interface(fn=generate_api, inputs=["text", "text", "number"], outputs="file")
-iface.launch(show_api=True, share=True)
+if __name__ == '__main__':
+    app.run(debug=True)