Update app.py

app.py

@@ -1,49 +1,3 @@
 import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
-from threading import Thread
 
-tokenizer = AutoTokenizer.from_pretrained("RAIJAY/7B_QA_68348")
-model = AutoModelForCausalLM.from_pretrained("RAIJAY/7B_QA_68348", torch_dtype=torch.float16)
-model = model.to('cuda:0')
-
-class StopOnTokens(StoppingCriteria):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        stop_ids = [29, 0]
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:
-                return True
-        return False
-
-def predict(message, history):
-
-    history_transformer_format = history + [[message, ""]]
-    stop = StopOnTokens()
-
-    messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]])  #curr_system_message +
-                for item in history_transformer_format])
-
-    model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
-    streamer = TextIteratorStreamer(tokenizer, timeout=10., 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=1000,
-        temperature=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:
-        if new_token != '<':
-            partial_message += new_token
-            yield partial_message
-
-
-gr.ChatInterface(predict).queue().launch()
+gr.Interface(fn=predict, inputs="text", outputs="text").launch()