Update app.py

app.py

@@ -1,47 +1,39 @@
 import gradio as gr
-from diffusers import StableDiffusionPipeline
-import os
-import cv2
+import requests
 
-# --- Initialize Stable Diffusion pipeline ---
-pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
+HF_API_TOKEN = "YOUR_HF_API_TOKEN"
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1"
 
-FRAME_FOLDER = "frames"
-VIDEO_OUTPUT = "generated_video.mp4"
+headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
 
-# --- Function to generate N frames using AI ---
-def generate_frames(prompt, num_frames=10):
-    os.makedirs(FRAME_FOLDER, exist_ok=True)
-    for i in range(num_frames):
-        image = pipe(prompt).images[0]
-        image.save(f"{FRAME_FOLDER}/frame_{i:03d}.png")
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.content
+
+def generate_video(prompt):
+    os.makedirs("frames", exist_ok=True)
+    for i in range(10):
+        image_bytes = query({"inputs": prompt})
+        with open(f"frames/frame_{i:03d}.png", "wb") as f:
+            f.write(image_bytes)
+
+    create_video_from_frames("frames", "generated_video.mp4", fps=2)
+    return "generated_video.mp4"
 
-# --- Function to create video from frames ---
 def create_video_from_frames(frame_folder, output_path, fps=2):
+    import cv2
     images = sorted([img for img in os.listdir(frame_folder) if img.endswith(".png")])
-    if not images:
-        raise ValueError("No frames generated.")
     frame = cv2.imread(os.path.join(frame_folder, images[0]))
     height, width, _ = frame.shape
-
     video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
     for img in images:
         video.write(cv2.imread(os.path.join(frame_folder, img)))
     video.release()
 
-# --- Main function called by Gradio ---
-def generate_video(prompt):
-    generate_frames(prompt, num_frames=10)  # Generate 10 frames
-    create_video_from_frames(FRAME_FOLDER, VIDEO_OUTPUT, fps=2)  # 2 fps
-    return VIDEO_OUTPUT
-
-# --- Gradio UI ---
-iface = gr.Interface(
-    fn=generate_video,
-    inputs=gr.Textbox(lines=3, placeholder="Describe your scene here..."),
-    outputs=gr.Video(),
-    title="AI Text-to-Video Generator (No manual assets needed)"
-)
+iface = gr.Interface(fn=generate_video,
+                     inputs=gr.Textbox(lines=3),
+                     outputs=gr.Video(),
+                     title="Text to Video AI")
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()