Add Image Animator (image-to-video) feature

Added new Image Animator tab: allows user to upload image + animation prompt, calls Hugging Face image-to-video model and returns animated mp4 output. Requires HF API key.

image_animator.py

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+import gradio as gr
+import requests
+import time
+from io import BytesIO
+
+# ---------- CONFIG ----------
+HF_API_TOKEN = "YOUR_HF_API_KEY"  # <-- yaha apna Hugging Face token daalna
+# Example image->video model (change if you prefer another)
+IMAGE_TO_VIDEO_MODEL = "ali-vilab/image-to-video"  
+API_URL = f"https://api-inference.huggingface.co/models/{IMAGE_TO_VIDEO_MODEL}"
+HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+# ----------------------------
+
+def call_image_to_video_api(image_bytes, prompt, steps=20):
+    """
+    Calls HF image->video model as multipart (image file + prompt).
+    Returns bytes (video) on success or None on failure.
+    """
+    files = {
+        "image": ("upload.png", image_bytes, "image/png")
+    }
+    data = {
+        "prompt": prompt,
+        # optional params: model-specific, keep conservative defaults
+        "num_inference_steps": str(steps)
+    }
+    try:
+        resp = requests.post(API_URL, headers=HEADERS, files=files, data=data, timeout=120)
+    except Exception as e:
+        return None, f"Request failed: {e}"
+
+    if resp.status_code == 200:
+        # Some HF inference endpoints return raw bytes, some return JSON with base64.
+        # Try to detect common cases.
+        content_type = resp.headers.get("content-type", "")
+        if "video" in content_type or content_type.startswith("application/octet-stream"):
+            return resp.content, None
+        # If JSON with base64
+        try:
+            js = resp.json()
+            if isinstance(js, dict) and "video" in js:
+                import base64
+                video_b64 = js["video"]
+                return base64.b64decode(video_b64), None
+        except Exception:
+            pass
+        return None, f"Unexpected response content-type: {content_type}"
+    else:
+        # try parse error
+        try:
+            err = resp.json()
+        except Exception:
+            err = resp.text
+        return None, f"Model error {resp.status_code}: {err}"
+
+def generate_animation(image, prompt, steps, progress=gr.Progress()):
+    """
+    Gradio handler: takes uploaded image (PIL or np array), prompt string and returns video bytes or error message.
+    """
+    if image is None or prompt is None or prompt.strip() == "":
+        return None, "Please upload an image and write a descriptive animation prompt."
+
+    # convert PIL image to PNG bytes
+    if hasattr(image, "save"):
+        img_bytes = BytesIO()
+        image.save(img_bytes, format="PNG")
+        img_bytes = img_bytes.getvalue()
+    else:
+        return None, "Invalid image format."
+
+    progress(0, desc="Sending to model...")
+    # call external HF model (this may take time)
+    video_bytes, error = call_image_to_video_api(img_bytes, prompt, steps=int(steps))
+    if error:
+        return None, f"Failed: {error}"
+
+    progress(100, desc="Done")
+    return BytesIO(video_bytes), None
+
+# ----- Gradio UI ----- (standalone tab / app)
+with gr.Blocks() as demo:
+    gr.Markdown("<h2 style='text-align:center'>✨ Image Animator — SaEdit MultiAi</h2>")
+    with gr.Row():
+        with gr.Column(scale=2):
+            upload = gr.Image(type="pil", label="Upload image (face/portrait/scene)")
+            prompt = gr.Textbox(label="Describe animation (e.g. 'slow zoom-in, gentle sparkles, 3s loop')", lines=3)
+            steps = gr.Slider(minimum=5, maximum=50, step=1, value=18, label="Inference steps (quality → speed)")
+            generate_btn = gr.Button("Generate Animation")
+            note = gr.Markdown(
+                "⚠️ Note: This uses a remote image→video model. Processing may take from 30s to a few minutes depending on model and load."
+            )
+        with gr.Column(scale=3):
+            out_video = gr.Video(label="Animated output (mp4)")
+            status = gr.Textbox(label="Status / Errors", interactive=False)
+
+    def on_click_generate(img, txt, steps_val):
+        video_file, err = generate_animation(img, txt, steps_val)
+        if err:
+            return None, str(err)
+        # return an IO object readable by Gradio as a video
+        return video_file, "Success — download below."
+
+    generate_btn.click(on_click_generate, [upload, prompt, steps], [out_video, status])
+
+# If you want to use this as a module and import the Blocks into your main app,
+# you can import demo and place it into a Tab.
+if __name__ == "__main__":
+    demo.launch()