add application file

deploy.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from transformers import pipeline
+
+# Load our Tokenizer
+tokenizer = AutoTokenizer.from_pretrained("huggingface/nlux/CodeLlama-7b-hf")
+
+# Load your model
+model = "./nlux/CodeLlama-7b-hf_merge"
+
+# load into pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def predict(input):
+    # Generate text using the pipeline
+    outputs = pipe(input, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.pad_token_id)
+    output = outputs[0]['generated_text'].strip()
+
+    # Print results
+    print(f"Generated Answer:\\n{output}")
+    return output
+
+# Create a Gradio interface
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+
+# Launch the interface
+iface.launch(share=True)
+