Create app.py

app.py

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+from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline
+import torch
+
+import gradio as gr
+
+# LLM helper functions
+def get_response_text(data):
+    text = data[0]["generated_text"]
+
+    assistant_text_index = text.rfind('### RESPONSE:')
+    if assistant_text_index != -1:
+        text = text[assistant_text_index+len('### RESPONSE:'):].strip()
+
+    return text
+
+def get_llm_response(prompt, pipe):
+    raw_output = pipe(prompt)
+    text = get_response_text(raw_output)
+    return text
+
+# Load LLM
+model_id = "georgesung/open_llama_7b_qlora_uncensored"
+tokenizer = LlamaTokenizer.from_pretrained(model_id)
+model = LlamaForCausalLM.from_pretrained(model_id, device_map="auto", load_in_8bit=True)
+
+# Llama tokenizer missing pad token
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_length=512,
+    temperature=0.7,
+    top_p=0.95,
+    repetition_penalty=1.15
+)
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    def hist_to_prompt(history):
+        prompt = ""
+        for human_text, bot_text in history:
+            prompt += f"### HUMAN:\n{human_text}\n\n### RESPONSE:\n"
+            if bot_text:
+                prompt += f"{bot_text}\n\n"
+        return prompt
+
+    def get_bot_response(text):
+        bot_text_index = text.rfind('### RESPONSE:')
+        if bot_text_index != -1:
+            text = text[bot_text_index + len('### RESPONSE:'):].strip()
+        return text
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        #bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
+        #history[-1][1] = bot_message + '</s>'
+
+        hist_text = hist_to_prompt(history)
+        print(hist_text)
+        bot_message = get_llm_response(hist_text, pipe) + tokenizer.eos_token
+        history[-1][1] = bot_message  # add bot message to overall history
+
+        return history
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue()
+demo.launch()
+