Update app.py

app.py

@@ -1,25 +1,31 @@
 import streamlit as st
-from transformers import CLIPTextModel, CLIPTokenizer
+from PIL import Image
 import torch
-from torch import autocast
-from diffusers import StableDiffusionPipeline
+from torchvision import transforms
+from your_model_library import YourModel  # Replace with actual model import
 
-# Load the model
-@st.cache(allow_output_mutation=True)
-def load_model():
-    model_id = "CompVis/stable-diffusion-v1-4"
-    device = "cpu"
-    pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True)
-    pipe.to(device)
-    return pipe
+# Load your lightweight model
+model = YourModel()
+model.load_state_dict(torch.load('path_to_model_weights.pth', map_location=torch.device('cpu')))
+model.eval()
 
-pipe = load_model()
+# Define image transformation
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+])
 
-# Streamlit app
-st.title("Text to Image Generator")
-prompt = st.text_input("Enter your prompt:", "A scenic landscape of mountains during sunset")
+st.title("Text-to-Image Generator")
+
+# Input text prompt
+prompt = st.text_input("Enter a text prompt:")
 
 if st.button("Generate Image"):
-    with st.spinner("Generating..."):
-        image = pipe(prompt)["sample"][0]
-        st.image(image, caption=prompt)
+    if prompt:
+        with torch.no_grad():
+            # Generate image from text prompt
+            generated_image = model.generate(prompt)  # Replace with actual generation method
+            generated_image = transform(generated_image).unsqueeze(0)
+            st.image(generated_image.squeeze().permute(1, 2, 0).numpy(), caption="Generated Image")
+    else:
+        st.warning("Please enter a text prompt.")