inital deployment1

app.py

@@ -2,6 +2,38 @@ import torch
 import gradio as gr
 from diffusers import StableDiffusionPipeline, LCMScheduler
 
+
+import gradio as gr
+import cv2
+import numpy as np
+
+# Function to process the video frame
+def process_frame(frame):
+    # Convert frame to grayscale (example processing step)
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    return gray_frame
+
+# Function to capture video feed
+def video_stream():
+    cap = cv2.VideoCapture(0)  # Open webcam
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        processed_frame = process_frame(frame)  # Apply processing
+        yield processed_frame  # Return processed frame
+    cap.release()
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=video_stream,
+    inputs=[],
+    outputs=gr.Video(label="Webcam Feed"),
+    live=True
+)
+
+
+
 # Load the pre-trained Real-Time LCM model
 model_id = "SimianLuo/LCM_Dreamshaper_v7"
 pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
@@ -13,14 +45,13 @@ def generate_image(prompt: str):
     image = pipe(prompt, num_inference_steps=4).images[0]
     return image
 
-# Create Gradio interface
-iface = gr.Interface(
-    fn=generate_image,
-    inputs=gr.Textbox(label="Enter a prompt"),
-    outputs=gr.Image(label="Generated Image"),
-    title="Real-Time LCM Image Generator",
-    description="Enter a prompt and get an AI-generated image in real time using Latent Consistency Models."
-)
-
 if __name__ == "__main__":
     iface.launch()
+
+
+
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch(share=True)
+