update

app.py

@@ -115,171 +115,201 @@
 
 ### 20aug
 
-import os
-import time
-import spaces
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
-import gradio as gr
-from threading import Thread
-
-MODEL_LIST = ["meta-llama/Meta-Llama-3.1-8B-Instruct"]
-HF_TOKEN = os.environ.get("HF_API_TOKEN", None)
-MODEL = os.environ.get("MODEL_ID")
-
-TITLE = "<h1><center>Meta-Llama3.1-8B</center></h1>"
-
-PLACEHOLDER = """
-<center>
-<p>Hi! How can I help you today?</p>
-</center>
-"""
-
-
-CSS = """
-.duplicate-button {
-    margin: auto !important;
-    color: white !important;
-    background: black !important;
-    border-radius: 100vh !important;
-}
-h3 {
-    text-align: center;
-}
-"""
-
-device = "cuda" # for GPU usage or "cpu" for CPU usage
-
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_compute_dtype=torch.bfloat16,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type= "nf4")
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL)
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    quantization_config=quantization_config)
-
-@spaces.GPU()
-def stream_chat(
-    message: str, 
-    history: list,
-    system_prompt: str,
-    temperature: float = 0.8, 
-    max_new_tokens: int = 1024, 
-    top_p: float = 1.0, 
-    top_k: int = 20, 
-    penalty: float = 1.2,
-):
-    print(f'message: {message}')
-    print(f'history: {history}')
-
-    conversation = [
-        {"role": "system", "content": system_prompt}
-    ]
-    for prompt, answer in history:
-        conversation.extend([
-            {"role": "user", "content": prompt}, 
-            {"role": "assistant", "content": answer},
-        ])
-
-    conversation.append({"role": "user", "content": message})
-
-    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device)
+# import os
+# import time
+# import spaces
+# import torch
+# from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
+# import gradio as gr
+# from threading import Thread
+
+# MODEL_LIST = ["meta-llama/Meta-Llama-3.1-8B-Instruct"]
+# HF_TOKEN = os.environ.get("HF_API_TOKEN", None)
+# MODEL = os.environ.get("MODEL_ID")
+
+# TITLE = "<h1><center>Meta-Llama3.1-8B</center></h1>"
+
+# PLACEHOLDER = """
+# <center>
+# <p>Hi! How can I help you today?</p>
+# </center>
+# """
+
+
+# CSS = """
+# .duplicate-button {
+#     margin: auto !important;
+#     color: white !important;
+#     background: black !important;
+#     border-radius: 100vh !important;
+# }
+# h3 {
+#     text-align: center;
+# }
+# """
+
+# device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+# quantization_config = BitsAndBytesConfig(
+#     load_in_4bit=True,
+#     bnb_4bit_compute_dtype=torch.bfloat16,
+#     bnb_4bit_use_double_quant=True,
+#     bnb_4bit_quant_type= "nf4")
+
+# tokenizer = AutoTokenizer.from_pretrained(MODEL)
+# model = AutoModelForCausalLM.from_pretrained(
+#     MODEL,
+#     torch_dtype=torch.bfloat16,
+#     device_map="auto",
+#     quantization_config=quantization_config)
+
+# @spaces.GPU()
+# def stream_chat(
+#     message: str, 
+#     history: list,
+#     system_prompt: str,
+#     temperature: float = 0.8, 
+#     max_new_tokens: int = 1024, 
+#     top_p: float = 1.0, 
+#     top_k: int = 20, 
+#     penalty: float = 1.2,
+# ):
+#     print(f'message: {message}')
+#     print(f'history: {history}')
+
+#     conversation = [
+#         {"role": "system", "content": system_prompt}
+#     ]
+#     for prompt, answer in history:
+#         conversation.extend([
+#             {"role": "user", "content": prompt}, 
+#             {"role": "assistant", "content": answer},
+#         ])
+
+#     conversation.append({"role": "user", "content": message})
+
+#     input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device)
     
-    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+#     streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
     
-    generate_kwargs = dict(
-        input_ids=input_ids, 
-        max_new_tokens = max_new_tokens,
-        do_sample = False if temperature == 0 else True,
-        top_p = top_p,
-        top_k = top_k,
-        temperature = temperature,
-        repetition_penalty=penalty,
-        eos_token_id=[128001,128008,128009],
-        streamer=streamer,
-    )
-
-    with torch.no_grad():
-        thread = Thread(target=model.generate, kwargs=generate_kwargs)
-        thread.start()
+#     generate_kwargs = dict(
+#         input_ids=input_ids, 
+#         max_new_tokens = max_new_tokens,
+#         do_sample = False if temperature == 0 else True,
+#         top_p = top_p,
+#         top_k = top_k,
+#         temperature = temperature,
+#         repetition_penalty=penalty,
+#         eos_token_id=[128001,128008,128009],
+#         streamer=streamer,
+#     )
+
+#     with torch.no_grad():
+#         thread = Thread(target=model.generate, kwargs=generate_kwargs)
+#         thread.start()
         
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        yield buffer
+#     buffer = ""
+#     for new_text in streamer:
+#         buffer += new_text
+#         yield buffer
 
             
-chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
-
-with gr.Blocks(css=CSS, theme="soft") as demo:
-    gr.HTML(TITLE)
-    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
-    gr.ChatInterface(
-        fn=stream_chat,
-        chatbot=chatbot,
-        fill_height=True,
-        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
-        additional_inputs=[
-            gr.Textbox(
-                value="You are a helpful assistant",
-                label="System Prompt",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0,
-                maximum=1,
-                step=0.1,
-                value=0.8,
-                label="Temperature",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=128,
-                maximum=8192,
-                step=1,
-                value=1024,
-                label="Max new tokens",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0.0,
-                maximum=1.0,
-                step=0.1,
-                value=1.0,
-                label="top_p",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=1,
-                maximum=20,
-                step=1,
-                value=20,
-                label="top_k",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0.0,
-                maximum=2.0,
-                step=0.1,
-                value=1.2,
-                label="Repetition penalty",
-                render=False,
-            ),
-        ],
-        examples=[
-            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
-            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
-            ["Tell me a random fun fact about the Roman Empire."],
-            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
-        ],
-        cache_examples=False,
-    )
+# chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
+
+# with gr.Blocks(css=CSS, theme="soft") as demo:
+#     gr.HTML(TITLE)
+#     gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
+#     gr.ChatInterface(
+#         fn=stream_chat,
+#         chatbot=chatbot,
+#         fill_height=True,
+#         additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+#         additional_inputs=[
+#             gr.Textbox(
+#                 value="You are a helpful assistant",
+#                 label="System Prompt",
+#                 render=False,
+#             ),
+#             gr.Slider(
+#                 minimum=0,
+#                 maximum=1,
+#                 step=0.1,
+#                 value=0.8,
+#                 label="Temperature",
+#                 render=False,
+#             ),
+#             gr.Slider(
+#                 minimum=128,
+#                 maximum=8192,
+#                 step=1,
+#                 value=1024,
+#                 label="Max new tokens",
+#                 render=False,
+#             ),
+#             gr.Slider(
+#                 minimum=0.0,
+#                 maximum=1.0,
+#                 step=0.1,
+#                 value=1.0,
+#                 label="top_p",
+#                 render=False,
+#             ),
+#             gr.Slider(
+#                 minimum=1,
+#                 maximum=20,
+#                 step=1,
+#                 value=20,
+#                 label="top_k",
+#                 render=False,
+#             ),
+#             gr.Slider(
+#                 minimum=0.0,
+#                 maximum=2.0,
+#                 step=0.1,
+#                 value=1.2,
+#                 label="Repetition penalty",
+#                 render=False,
+#             ),
+#         ],
+#         examples=[
+#             ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
+#             ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
+#             ["Tell me a random fun fact about the Roman Empire."],
+#             ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
+#         ],
+#         cache_examples=False,
+#     )
+
+
+# if __name__ == "__main__":
+#     demo.launch()
 
+import os
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+
+# Your Hugging Face configuration
+model_name = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+# token = "hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
+
+# Initialize Inference Client with model and token
+inference_client = InferenceClient()
+
+def chat_completion(prompt):
+    # Pass user input through Hugging Face model
+    response = inference_client.chat(
+        model=model_name,
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=500,
+        stream=False
+    )
+    
+    # Extract content from the response
+    response_text = response['choices'][0]['delta']['content']
+    
+    return response_text
 
-if __name__ == "__main__":
-    demo.launch()
+# Create Gradio chat interface
+chatbot = gr.ChatInterface(fn=chat_completion)
+chatbot.launch()