Update app.py

app.py

@@ -1,90 +1,66 @@
-import spaces
-import os
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from transformers import TextStreamer
 import gradio as gr
+from huggingface_hub import InferenceClient
 
-text_generator = None
-is_hugging_face = True
-model_id = "google/gemma-2-9b-it"
-model_id = "google/gemma-2-2b-it"
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-device = "auto" # torch.device("cuda" if torch.cuda.is_available() else "cpu")
-device = "cuda"
-dtype = torch.bfloat16
-
-if not huggingface_token:
-    pass
-    print("no HUGGINGFACE_TOKEN if you need set secret ")
-    #raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")
-        
-
-    
-    
-    
-
-
-
-tokenizer = AutoTokenizer.from_pretrained(model_id, token=huggingface_token)
-
-print(model_id,device,dtype)
-histories = []
-#model = None
-
-
-
-if not is_hugging_face:
-    model = AutoModelForCausalLM.from_pretrained(
-        model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
-    )
-    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device,stream=True ) #pipeline has not to(device)
-    
-    if next(model.parameters()).is_cuda:
-        print("The model is on a GPU")
-    else:
-        print("The model is on a CPU")
-
-    #print(f"text_generator.device='{text_generator.device}")
-    if str(text_generator.device).strip() == 'cuda':
-        print("The pipeline is using a GPU")
-    else:
-        print("The pipeline is using a CPU")
-
-print("initialized")
+"""
+For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+"""
+client = InferenceClient("google/gemma-2-2b")
 
 @spaces.GPU(duration=60)
-def generate_text(messages):
-    if is_hugging_face:#need everytime initialize for ZeroGPU
-        model = AutoModelForCausalLM.from_pretrained(
-                model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
-            )
-    streamer = TextStreamer(tokenizer, skip_prompt=True)    
-    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device ,streamer=streamer,model_kwargs={"stream": True} ) #pipeline has not to(device)
-    result = text_generator(messages, max_new_tokens=256, do_sample=True, temperature=0.7)
-    print(f"result={result}")
-    generated_output = ""
-    for token in result:
-        print(f"token={token}")
-        generated_output += token["text"]
-        yield generated_output
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+    response = ""
+
+    # Load model directly
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
 
 
-def call_generate_text(message, history):
-   # history.append({"role": "user", "content": message})
-    print(message)
-    print(history)
-   
-    messages = history+[{"role":"user","content":message}]
-    try:
-        
-        for text in generate_text(messages):
-            yield text
-    except RuntimeError  as e:
-        print(f"An unexpected error occurred: {e}")
-        yield ""
-
-demo = gr.ChatInterface(call_generate_text,type="messages")
-
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()
+
+