init

app.py

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+import spaces
+import os
+
+from huggingface_hub import Repository
+from huggingface_hub import login
+
+init_feedback = False
+
+try:
+    login(token = os.environ['HUB_TOKEN'])
+
+    repo = Repository(
+        local_dir="backend_fn",
+        repo_type="dataset",
+        clone_from=os.environ['DATASET'],
+        token=True,
+        git_email='zhiheng_dev@dahreply.ai'
+    )
+    repo.git_pull()
+
+    init_feedback = True
+except:
+    pass
+
+import json
+import uuid
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from threading import Thread
+
+if init_feedback:
+    from backend_fn.feedback import feedback
+
+from gradio_modal import Modal
+
+"""
+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
+"""
+model_name = "Merdeka-LLM/merdeka-llm-hr-3b-128k-instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+streamer = TextIteratorStreamer(tokenizer, timeout=300, skip_prompt=True, skip_special_tokens=True)
+
+histories = []
+action = None
+feedback_index = None
+
+session_id = uuid.uuid1().__str__()
+
+@spaces.GPU
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    # system_message,
+    max_tokens = 4096,
+    temperature = 0.01,
+    top_p = 0.95,
+):
+    messages = [
+        {"role": "system", "content": "You are a professional lawyer who is familiar with Malaysia Law."}
+    ]
+
+    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 = ""
+
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+    generate_kwargs = dict(
+        model_inputs,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        streamer=streamer
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    for new_token in streamer:
+      if new_token != '<':
+          response += new_token
+          yield response
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+
+def submit_feedback(value):
+    feedback(session_id, json.dumps(histories), value, action, feedback_index)
+
+
+with gr.Blocks() as demo:
+    def vote(history,data: gr.LikeData):
+        global histories
+        global action
+        global feedback_index
+        histories = history
+        action = data.liked
+        feedback_index = data.index[0]
+
+    with Modal(visible=False) as modal:
+        textb = gr.Textbox(
+            label='Actual response',
+            info='Leave blank if the answer is good enough'
+        )
+
+        submit_btn = gr.Button(
+            'Submit'
+        )
+
+        submit_btn.click(submit_feedback,textb)
+        submit_btn.click(lambda: Modal(visible=False), None, modal)
+        submit_btn.click(lambda x: gr.update(value=''), [],[textb])
+
+
+    ci = gr.ChatInterface(
+        respond,
+        description='Due to an unknown bug in Gradio, we are unable to expand the conversation section to full height.'
+        # fill_height=True
+        # 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=0.1, step=0.1, label="Temperature"),
+        #     gr.Slider(
+        #         minimum=0.1,
+        #         maximum=1.0,
+        #         value=0.95,
+        #         step=0.05,
+        #         label="Top-p (nucleus sampling)",
+        #     ),
+        # ],
+    )
+
+
+    ci.chatbot.show_copy_button=True
+    # ci.chatbot.value=[(None,"Hello! I'm here to assist you with understanding the laws and acts of Malaysia.")]
+    # ci.chatbot.height=500
+
+    if init_feedback:
+        ci.chatbot.like(vote, ci.chatbot, None).then(
+            lambda: Modal(visible=True), None, modal
+        )
+
+if __name__ == "__main__":
+    demo.launch(
+        
+    )
+ 

requirements.txt

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+accelerate==1.0.1
+aiofiles==23.2.1
+annotated-types==0.7.0
+anyio==4.6.2.post1
+certifi==2024.8.30
+charset-normalizer==3.4.0
+click==8.1.7
+fastapi==0.115.4
+ffmpy==0.4.0
+filelock==3.16.1
+fsspec==2024.10.0
+gradio==5.4.0
+gradio_client==1.4.2
+gradio_modal==0.0.4
+h11==0.14.0
+httpcore==1.0.6
+httpx==0.27.2
+huggingface-hub==0.26.2
+idna==3.10
+Jinja2==3.1.4
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.4.2
+numpy==1.26.4
+orjson==3.10.10
+packaging==24.1
+pandas==2.2.3
+pillow==11.0.0
+psutil==5.9.8
+pydantic==2.9.2
+pydantic_core==2.23.4
+pydub==0.25.1
+Pygments==2.18.0
+PyMySQL==1.1.1
+python-dateutil==2.9.0.post0
+python-multipart==0.0.12
+pytz==2024.2
+PyYAML==6.0.2
+regex==2024.9.11
+requests==2.32.3
+rich==13.9.3
+ruff==0.7.1
+safehttpx==0.1.1
+safetensors==0.4.5
+semantic-version==2.10.0
+setuptools==75.3.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+spaces==0.30.4
+starlette==0.41.2
+sympy==1.13.1
+tokenizers==0.20.1
+tomlkit==0.12.0
+torch==2.2.0
+tqdm==4.66.6
+transformers==4.46.1
+typer==0.12.5
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3
+uvicorn==0.32.0
+websockets==12.0