update app.py

app.py

@@ -1,7 +1,90 @@
+# import gradio as gr
+# import torch
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# def load_model():
+#     model = AutoModelForCausalLM.from_pretrained("mattshumer/mistral-8x7b-chat", trust_remote_code=True)
+#     tok = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat")
+#     return model, tok
+
+# def inference(model, tok, PROMPT):
+#     x = tok.encode(PROMPT, return_tensors="pt").cuda()
+#     x = model.generate(x, max_new_tokens=512).cpu()
+#     return tok.batch_decode(x)
+
+
+# gr.ChatInterface(inference).queue().launch()
+
+
 import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+from threading import Thread
+
+tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
+model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", 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()
+
+
+
+def predict(message, history):
+    history_openai_format = []
+    for human, assistant in history:
+        history_openai_format.append({"role": "user", "content": human })
+        history_openai_format.append({"role": "assistant", "content":assistant})
+    history_openai_format.append({"role": "user", "content": message})
+
+    response = openai.ChatCompletion.create(
+        model='gpt-3.5-turbo',
+        messages= history_openai_format,
+        temperature=1.0,
+        stream=True
+    )
 
-def greet(name):
-    return "Hello " + name + "!!"
+    partial_message = ""
+    for chunk in response:
+        if len(chunk['choices'][0]['delta']) != 0:
+            partial_message = partial_message + chunk['choices'][0]['delta']['content']
+            yield partial_message
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()