changes

Text2image-api.py

@@ -1,81 +0,0 @@
-from flask import Flask, jsonify, request
-from pathlib import Path
-import sys
-import torch
-import os
-from torch import autocast
-from diffusers import StableDiffusionPipeline, DDIMScheduler
-import streamlit as st
-
-# model_path = WEIGHTS_DIR             # If you want to use previously trained model saved in gdrive, replace this with the full path of model in gdrive
-
-# pipe = StableDiffusionPipeline.from_pretrained(model_path, safety_checker=None, torch_dtype=torch.float32).to("cuda")
-# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-# pipe.enable_xformers_memory_efficient_attention()
-# g_cuda = None
-         
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))
-
-app = Flask(__name__)
-  
-# @app.route('/', methods = ['GET', 'POST'])
-# def home():
-#     if(request.method == 'GET'):
-  
-#         data = "Text2Image"
-#         return jsonify({'service': data})
-  
-
-# @app.route("/", methods=["POST"])
-def generate():
-
-    # prompt = request.form['prompt']
-    # negative_prompt = request.form['Negative prompt']
-    # num_samples = request.form['No. of samples']
-
-    prompt = st.text_area(label = "prompt", key="pmpt")
-    negative_prompt = st.text_area(label = "Negative prompt", key="ng_pmpt")
-    num_samples = st.number_input("No. of samples")
-
-    res = st.button("Reset", type="primary")
-
-    if res:
-
-        guidance_scale = 7.5
-        num_inference_steps = 24
-        height = 512
-        width = 512
-
-        g_cuda = torch.Generator(device='cuda')
-        seed = 52362
-        g_cuda.manual_seed(seed)
-
-        # commandline_args = os.environ.get('COMMANDLINE_ARGS', "--skip-torch-cuda-test --no-half")
-
-        with autocast("cuda"), torch.inference_mode():
-            images = pipe(
-                prompt,
-                height=height,
-                width=width,
-                negative_prompt=negative_prompt,
-                num_images_per_prompt=num_samples,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=guidance_scale,
-                generator=g_cuda
-            ).images
-        
-        return {"message": "successful"}
-    
-    else:
-        return {"message": "Running.."}
-
-
-
-  
-# driver function
-if __name__ == '__main__':
-    generate()

app.py

@@ -37,8 +37,8 @@ def generate():
     # negative_prompt = request.form['Negative prompt']
     # num_samples = request.form['No. of samples']
 
-    prompt = st.text_area(placeholder = "prompt", key="pmpt")
-    negative_prompt = st.text_area(placeholder = "Negative prompt", key="ng_pmpt")
+    prompt = st.text_area(label = "prompt", key="pmpt")
+    negative_prompt = st.text_area(label = "Negative prompt", key="ng_pmpt")
     num_samples = st.number_input("No. of samples")
 
     res = st.button("Reset", type="primary")