Update requirements.txt

requirements.txt

@@ -1,49 +1,11 @@
-import streamlit as st
-from PIL import Image
-import torch
-from diffusers import AutoPipelineForInpainting
-from diffusers.utils import load_image
-
-pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16")
-generator = torch.Generator(device="cuda").manual_seed(0)
-
-def main():
-    st.title("Image and Text Preview App")
-
-    # Upload images
-    st.sidebar.subheader("Upload Images")
-    uploaded_image1 = st.sidebarfile_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-    uploaded_image2 = st.sidebarfile_uploader("Upload Mask", type=["jpg", "jpeg", "png"])
-
-    # Input text
-    st.subheader("Text Prompts")
-    prompt = st.text_input("Text prompt", "")
-    negative_prompt = st.text_input("Negative prompt", "")
-
-    guidance_scale = st.text_input("Guidance scale", 7.5)
-    steps = st.text_input("Steps", 20)
-    strength = st.text_input("Strength", 1)
-    
-    btn_submit = st.button("Generate")
-     
-    if btn_submit:
-        init_image = Image.open(uploaded_image1).convert("RGB")
-        st.image(init_image, caption="Uploaded Image", use_column_width=True)
-        mask_image = Image.open(uploaded_image2).convert("RGB")
-        st.image(mask_image, caption="Uploaded Mask", use_column_width=True)
-    
-        output = pipe(prompt = prompt, 
-                      negative_prompt=negative_prompt, 
-                      image=init_image, 
-                      mask_image=mask_image, 
-                      guidance_scale=int(guidance_scale), 
-                      num_inference_steps=int(steps), 
-                      strength=strength,
-                      generator=generator)
-        
-        st.text("Generated Image:")
-        st.image(output.images[0])
-
-
-if __name__ == "__main__":
-    main()
+diffusers==0.22.1
+matplotlib
+seaborn
+Pillow
+opencv-python
+scikit-image
+transformers
+pandas
+numpy
+torch
+streamlit