Create App.py

app.py

@@ -0,0 +1,52 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+import torch
+
+# Load the model and tokenizer
+model_name = "Skywork/SkyReels-V2-DF-1.3B-540P"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Set device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+def generate_video(prompt, max_length=512, temperature=0.7, top_k=50, top_p=0.95):
+    """
+    Generate video based on text prompt
+    """
+    # Tokenize input
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    
+    # Generate output
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=max_length,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            do_sample=True
+        )
+    
+    # Decode and return the output
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # In a real implementation, you would process this into a video
+    # For demo purposes, we'll just return the generated text
+    return generated_text
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_video,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your video prompt here..."),
+    outputs="text",
+    title="SkyReels Video Generation",
+    description="Generate video content using Skywork/SkyReels-V2-DF-1.3B-540P model",
+    examples=[
+        ["A sunny day at the beach with waves crashing"],
+        ["A futuristic cityscape at night with flying cars"]
+    ]
+)
+
+iface.launch()