Create app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("BAAI/Video-XL-2", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("BAAI/Video-XL-2", trust_remote_code=True)
+
+# Inference function
+def generate_response(prompt, max_new_tokens=100):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            top_k=50,
+            top_p=0.95,
+            temperature=0.7,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response[len(prompt):].strip()
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(label="Enter your prompt", lines=4, placeholder="Ask me something..."),
+        gr.Slider(minimum=10, maximum=300, step=10, value=100, label="Max New Tokens"),
+    ],
+    outputs=gr.Textbox(label="Response"),
+    title="Video-XL-2 Chatbot",
+    description="This chatbot uses the BAAI Video-XL-2 model to generate responses based on your input."
+)
+
+if __name__ == "__main__":
+    iface.launch()