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app.py

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+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+
+# Load the BAGEL model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/BAGEL-7B-MoT")
+model = AutoModel.from_pretrained("ByteDance-Seed/BAGEL-7B-MoT")
+
+# Define a function to generate outputs
+def generate(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create a Gradio interface
+demo = gr.Interface(
+    fn=generate,  # The function to run
+    inputs="text",  # Input type (text box)
+    outputs="text"  # Output type (text box)
+)
+
+# Launch the app
+demo.launch()

requirements.txt

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+gradio
+transformers
+torch