Create app.py

app.py

@@ -0,0 +1,96 @@
+import os
+
+os.system('pip install torch')
+os.system('pip install transformers==4.37.1')
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+from threading import Thread
+
+model_path = 'sail/Sailor-7B-Chat'
+
+# Loading the tokenizer and model from Hugging Face's model hub.
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
+
+# using CUDA for an optimal experience
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+
+# Defining a custom stopping criteria class for the model's text generation.
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [151645]  # IDs of tokens where the generation should stop.
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:  # Checking if the last generated token is a stop token.
+                return True
+        return False
+
+
+system_role= 'system'
+user_role = 'question'
+assistant_role = "answer"
+
+sft_start_token =  "<|im_start|>"
+sft_end_token = "<|im_end|>"
+ct_end_token = "<|endoftext|>"
+
+system_prompt= \
+'You are an AI assistant named Sailor created by Sea AI Lab. \
+Your answer should be friendly, unbiased, faithful, informative and detailed.'
+system_prompt = f"<|im_start|>{system_role}\n{system_prompt}<|im_end|>"
+
+# Function to generate model predictions.
+def predict(message, history):
+    # history = []
+    history_transformer_format = history + [[message, ""]]
+    stop = StopOnTokens()
+
+    # Formatting the input for the model.
+    messages =  system_prompt + sft_end_token.join([sft_end_token.join([f"\n{sft_start_token}{user_role}\n" + item[0], f"\n{sft_start_token}{assistant_role}\n" + item[1]])
+                        for item in history_transformer_format])
+    model_inputs = tokenizer([messages], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=256,
+        do_sample=True,
+        top_p= 0.75,
+        top_k= 60,
+        temperature=0.2,
+        num_beams=1,
+        stopping_criteria=StoppingCriteriaList([stop]),
+        repetition_penalty=1.1,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()  # Starting the generation in a separate thread.
+    partial_message = ""
+    for new_token in streamer:
+        partial_message += new_token
+        if sft_end_token in partial_message:  # Breaking the loop if the stop token is generated.
+            break
+        yield partial_message
+
+
+css = """
+full-height {
+    height: 100%;
+}
+"""
+
+prompt_examples = [
+    'How to cook a fish?',
+    'Cara memanggang ikan',
+    'วิธีย่างปลา',
+    'Cách nướng cá'
+]
+
+with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:
+    gr.Markdown("""<center><font size=8>Sailor-Chat Bot⚓</center>""")
+    gr.ChatInterface(predict, fill_height=True, examples=prompt_examples, css=css)
+    gr.Markdown("""<p align="center"><img src="https://github.com/sail-sg/sailor-llm/raw/main/misc/banner.jpg" style="height: 180px"/><p>""")
+    
+
+    demo.launch(share=True)  # Launching the web interface.