Create app.py

app.py

@@ -0,0 +1,71 @@
+# demo5
+# tttt
+import os
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+from transformers import is_torch_npu_available
+from threading import Thread
+
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-14B-Chat")
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-14B-Chat", torch_dtype=torch.bfloat16)
+if is_torch_npu_available():
+    model.to("npu:0")
+elif torch.cuda.is_available():
+    mode.to("cuda:0")
+
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [2]
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+
+def predict(message, history):
+    #if is_torch_npu_available():
+    #    torch.npu.set_device(model.device)
+    stop = StopOnTokens()
+    conversation = []
+
+    for user, assistant in history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+
+    conversation.append({"role": "user", "content": message})
+    print(f'>>>conversation={conversation}', flush=True)
+    prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
+    model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    streamer = TextIteratorStreamer(tokenizer, timeout=100., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=1024,
+        do_sample=True,
+        top_p=0.95,
+        top_k=50,
+        temperature=0.7,
+        repetition_penalty=1.0,
+        num_beams=1,
+        stopping_criteria=StoppingCriteriaList([stop])
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    partial_message = ""
+    for new_token in streamer:
+        partial_message += new_token
+        if '</s>' in partial_message:
+            break
+        yield partial_message
+
+
+# Setting up the Gradio chat interface.
+gr.ChatInterface(predict,
+                 title="Qwen1.5 0.5B Chat Demo",
+                 description="Warning. All answers are generated and may contain inaccurate information.",
+                 examples=['How do you cook fish?', 'Who is the president of the United States?']
+                 ).launch()
+