Create app.py

app.py

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+import gradio as gr
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# The huggingface model id for Microsoft's phi-2 model
+checkpoint = "microsoft/phi-2"
+
+# Download and load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True)
+
+# Text generation pipeline
+phi2 = pipeline("text-generation", tokenizer=tokenizer, model=model)
+
+# Function that accepts a prompt and generates text using the phi2 pipeline
+def generate(prompt, chat_history):
+
+  instruction = "You are a helpful assistant to 'User'. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'."
+  final_prompt = f"Instruction: {instruction}\n"
+
+  for sent, received in chat_history:
+    final_prompt += "User: " + sent + "\n"
+    final_prompt += "Assistant: " + received + "\n"
+
+  final_prompt += "User: " + prompt + "\n"
+  final_prompt += "Output:"
+
+  generated_text = phi2(final_prompt, max_length=256)[0]["generated_text"]
+  response = generated_text.split("Output:")[1].split("User:")[0]
+
+  if "Assistant:" in response:
+    response = response.split("Assistant:")[1].strip()
+
+  chat_history.append((prompt, response))
+
+  return "", chat_history
+
+# Chat interface with gradio
+with gr.Blocks() as demo:
+  gr.Markdown("# Phi-2 Chatbot Demo")
+
+  chatbot = gr.Chatbot()
+  msg = gr.Textbox()
+
+  clear = gr.ClearButton([msg, chatbot])
+  msg.submit(fn=generate, inputs=[msg, chatbot], outputs=[msg, chatbot])
+
+demo.launch()