Update app.py

app.py

@@ -1,31 +1,76 @@
-import streamlit as st
-import requests
+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+from PIL import Image
+import time
 
-# Streamlit UI for uploading content
-st.title("Facebook Auto-Post Scheduler")
+# Load CPU-optimized model
+model_id = "OFA-Sys/small-stable-diffusion-v0"  # Smaller model for CPU
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32  # Force float32 for CPU
+)
 
-# User input for content
-message = st.text_area("Message for Post", "Hello, this is an automated post!")
-file = st.file_uploader("Upload an Image or Video", type=["jpg", "jpeg", "png", "mp4"])
+# Use DPMSolver for better CPU performance
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cpu")
 
-# Replace with your ngrok URL from Colab
-COLAB_URL = 'https://your-colab-url.ngrok.io'  # Example: https://abc123.ngrok.io
+def generate_image(text):
+    if not text:
+        return None, "Please enter some text first!"
+    
+    start_time = time.time()
+    
+    try:
+        # Generate with reduced steps for faster processing
+        image = pipe(
+            text,
+            num_inference_steps=20,  # Reduced from typical 50 steps
+            guidance_scale=7.5
+        ).images[0]
+        
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+            
+        gen_time = time.time() - start_time
+        return image, f"Generated in {gen_time:.1f} seconds"
+    
+    except Exception as e:
+        return None, f"Error: {str(e)}"
 
-if st.button("Schedule Post"):
-    if file:
-        # Upload media to backend API (Colab)
-        files = {'file': file.getvalue()}
-        response = requests.post(f"{COLAB_URL}/upload", files=files)
-        if response.status_code == 200:
-            st.success("Media uploaded successfully!")
-        else:
-            st.error(f"Failed to upload media: {response.text}")
+# Create Gradio interface with loading states
+with gr.Blocks(title="CPU Poetry to Image") as demo:
+    gr.Markdown("# 💖 CPU-Friendly Poetry to Image")
+    gr.Markdown("Note: Generation may take 2-5 minutes on CPU")
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Your Romantic Text",
+                placeholder="e.g., 'Your eyes sparkle like stars'",
+                lines=3
+            )
+            generate_btn = gr.Button("Create Magic ✨")
+            
+        with gr.Column():
+            output_image = gr.Image(label="Your Generated Art")
+            time_info = gr.Textbox(label="Generation Time")
+    
+    examples = gr.Examples(
+        examples=[
+            ["A moonlit beach with heart-shaped waves"],
+            ["Two roses intertwined with golden light"],
+            ["A love letter floating in the clouds"]
+        ],
+        inputs=[input_text]
+    )
+    
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[input_text],
+        outputs=[output_image, time_info],
+        api_name="generate"
+    )
 
-    if message:
-        # Send message to backend API (Colab)
-        data = {'message': message}
-        response = requests.post(f"{COLAB_URL}/post", json=data)
-        if response.status_code == 200:
-            st.success("Post scheduled successfully!")
-        else:
-            st.error(f"Failed to schedule post: {response.text}")
+if __name__ == "__main__":
+    demo.launch()