Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+model_name = "DAMO-NLP-SG/VideoLLaMA3-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+model.eval()
+
+def generate_response(prompt, max_tokens=200, temperature=0.7):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            do_sample=True,
+            top_p=0.9,
+            eos_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response[len(prompt):].strip()  # Return only the generated part
+
+# Gradio UI
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(label="Prompt", lines=5, placeholder="Enter your prompt here..."),
+        gr.Slider(minimum=50, maximum=1000, value=200, label="Max Tokens"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
+    ],
+    outputs=gr.Textbox(label="Response"),
+    title="VideoLLaMA3-7B Text Generation",
+    description="Generate text using DAMO-NLP-SG/VideoLLaMA3-7B"
+)
+
+if __name__ == "__main__":
+    iface.launch()