Update app.py

app.py

@@ -1,110 +1,30 @@
-import json
+import requests
 import os
-import random
-import time
-from pathlib import Path
-
 import gradio as gr
-import requests
-
-from PIL import Image
-
-
-BASE_FOLDER = Path("")
-OUTPUT_DIR = Path("")
-URL = "https://c253-2405-201-300a-51fe-6553-e08f-1bce-578f.ngrok-free.app/prompt"
-TXT2IMG = "workflow_api_text2img.json"
-IMG2IMG = "workflow_api_img2img.json"
-HIRES = "workflow_api_HiRes.json"
-
-KSAMPLER_NAMES = [
-    "euler",
-    "euler_ancestral",
-    "dpmpp_sde",
-    "dpmpp_2m",
-    "dpmpp_2m_sde",
-    "dpmpp_3m_sde",
-    "ddpm",
-    "lcm",
-]
-
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
-
-
-css = """
-.green {
-    border: 3px solid green !important;
-}
-.red {
-    border: 3px solid red !important;
-}
-"""
-
-
-def checkpoint_list():
-    return [model.name for model in BASE_FOLDER.glob("*.ckpt")]
-
-
-
-def get_latest_image():
-    image_files = list(OUTPUT_DIR.glob("*.png"))
-    image_files.sort(key=lambda x: os.path.getmtime(x))
-    latest_image = image_files[-1] if image_files else None
-    return latest_image
-
-
-def start_queue(prompt_workflow):
-    p = {"prompt": prompt_workflow}
-    data = json.dumps(p).encode("utf-8")
-    requests.post(URL, data=data)
-
-
-def txt2img_workflow(checkpoint_name, positive_prompt):
-    with open(TXT2IMG, "r") as file_json:
-        prompt = json.load(file_json)
-
-    prompt["1"]["inputs"]["ckpt_name"] = checkpoint_name
-    prompt["2"]["inputs"]["noise_seed"] = random.randint(1, 999999999999)
-    prompt["3"]["inputs"]["text"] = positive_prompt
-
-    print( prompt["2"]["inputs"]["noise_seed"])
-    previous_image = get_latest_image()
-
-    start_queue(prompt)
-    return True
-    """while True:
-        latest_image = get_latest_image()
-        if latest_image != previous_image:
-            return latest_image
+import json
 
-        time.sleep(1)
-    """
+# Replace the empty string with your model id below
+model_id = "7qrmoo93"
+baseten_api_key = "gN3ksgBu.ayDr4GN8iTBEzoQu5pQjmNz6zuj0iL8t"
 
+def res(data):
+    print(data)
+    resp = requests.post("https://model-7qrmoo93.api.baseten.co/production/predict",headers={"Authorization": "Api-Key V5r36lUR.s8F3b2Amna9PnQRyP3lFswULHYVF86ki"},json={'top_p': 0.75, 'prompt': data, 'num_beams': 4, 'temperature': 0.4},)
+    print(resp.json()) 
+    return resp.json()
 
 def main():
-    with gr.Blocks(css=css) as demo:
-        models = checkpoint_list()
-
+    with gr.Blocks() as demo:
         with gr.Row():
-            with gr.Column():
-                base_checkpoint = gr.Dropdown(choices=models, label="Stable Diffusion Checkpoint")
-
-        with gr.Tab("txt2img"):
-            workflow = TXT2IMG
-            with gr.Row():
-                with gr.Column(scale=3):
-                    positive = gr.Textbox(lines=4, placeholder="Positive prompt", container=False, elem_classes="green")
-
-                with gr.Column(scale=1):
-                    generate_btn_txt2img = gr.Button("Generate")
-
-            with gr.Row():
-                output_img = gr.Image(label="Output", interactive=False)
-
-        generate_btn_txt2img.click(fn=txt2img_workflow, inputs=[base_checkpoint, positive], outputs=output_img)
-
+            with gr.Column(scale=3):
+                text_input = gr.Textbox()
+        with gr.Row():
+            with gr.Column(scale=1):
+                text_output = gr.Textbox()
+        with gr.Row():
+            text_button = gr.Button("Send")
+        text_button.click(res, text_input, text_output)
     demo.launch()
 
-
 if __name__ == "__main__":
-    main()
+    main()