Update app.py

app.py

@@ -1,18 +1,47 @@
 import gradio
 
-def my_inference_function(name):
-  return "Hello " + name + "!"
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+MODEL_NAME = "arnir0/Tiny-LLM"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+def generate_text(prompt, model, tokenizer, max_length=4096, temperature=0.8, top_k=50, top_p=0.95):
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+
+    outputs = model.generate(
+        inputs,
+        max_length=max_length,
+        temperature=temperature,
+        top_k=top_k,
+        top_p=top_p,
+        do_sample=True
+    )
+
+
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+
+def my_inference_function(text):
+    prompt = f"Summary the context below\n\n{text}"
+    generated_text = generate_text(prompt, model, tokenizer)
+    
+    return generated_text[len(prompt):]
 
 gradio_interface = gradio.Interface(
-  fn=my_inference_function,
-  inputs="text",
-  outputs="text",
-  examples=[
+    fn=my_inference_function,
+    inputs="text",
+    outputs="text",
+    examples=[
     ["Jill"],
     ["Sam"]
-  ],
-  title="REST API with Gradio and Huggingface Spaces",
-  description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! Based on [this article](https://www.tomsoderlund.com/ai/building-ai-powered-rest-api). See the **Use via API** link at the bottom of this page.",
-  article="© Tom Söderlund 2022"
+    ],
+    title="REST API with Gradio and Huggingface Spaces",
+    description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! Based on [this article](https://www.tomsoderlund.com/ai/building-ai-powered-rest-api). See the **Use via API** link at the bottom of this page.",
+    article="© Tom Söderlund 2022"
 )
 gradio_interface.launch()