Init

app.py

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+from threading import Thread
+from typing import Iterator
+
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = 4096
+
+DESCRIPTION = """\
+# Llama-2 7B Chat
+
+This Space demonstrates model [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta, a Llama 2 model with 7B parameters fine-tuned for chat instructions. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).
+
+🔎 For more details about the Llama 2 family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/llama2).
+
+🔨 Looking for an even more powerful model? Check out the [13B version](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat) or the large [70B model demo](https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI).
+"""
+
+LICENSE = """
+<p/>
+
+---
+As a derivate work of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta,
+this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/USE_POLICY.md).
+"""
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+
+
+if torch.cuda.is_available():
+    model_id = "DAMO-NLP-SG/CLEX-7b-Chat-16K"
+    # from CLEX import LlamaForCausalLM
+    from transformers import AutoModelForCausalLM
+    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    tokenizer.use_default_system_prompt = False
+
+import PyPDF2
+from io import BytesIO
+
+def process_pdf(input_pdf):
+    # Read the binary data from the input_pdf
+    # pdf_data = BytesIO(input_pdf)
+    # if pdf_data.getvalue().strip() == b'':
+    #     return ""
+    # Create a PDF reader object
+    reader = PyPDF2.PdfReader(input_pdf.name)
+    # Extract the text from each page of the PDF
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    # Close the PDF reader and reset the pointer
+    # reader.close()
+    # pdf_data.seek(0)
+    # Return the extracted text
+    return text
+
+
+
+def build_chat():
+    from fastchat.model import get_conversation_template
+    conv = get_conversation_template("vicuna")
+    conv.append_message(conv.roles[0], prompt)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt() 
+    return prompt
+
+@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    system_prompt: str,
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    conversation = []
+    if system_prompt:
+        conversation.append({"role": "system", "content": system_prompt})
+    for user, assistant in chat_history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    conversation.append({"role": "user", "content": message})
+
+    chat = tokenizer.apply_chat_template(conversation, tokenize=False)
+    inputs = tokenizer(chat, return_tensors="pt", add_special_tokens=False).to("cuda")
+    if len(inputs) > MAX_INPUT_TOKEN_LENGTH:
+        inputs = inputs[-MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning("Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+
+def generate_with_pdf(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    system_prompt: str,
+    input_pdf: BytesIO = None,
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    if input_pdf is not None:
+        pdf_text = process_pdf(input_pdf)
+        # print(pdf_text)
+        message += f"\nThis is the beginning of a pdf\n{pdf_text}This is the end of a pdf\n"
+    yield from generate(
+        message,
+        chat_history,
+        system_prompt,
+        max_new_tokens,
+        temperature,
+        top_p,
+        top_k,
+        repetition_penalty
+    )
+
+chat_interface = gr.ChatInterface(
+    fn=generate_with_pdf,
+    additional_inputs=[
+        gr.Textbox(label="System prompt", lines=6),
+        gr.File(label="PDF File", accept=".pdf"),
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
+            minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.9,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.2,
+        ),
+    ],
+    stop_btn=None,
+    examples=[
+        ["Hello there! How are you doing?"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["Explain the plot of Cinderella in a sentence."],
+        ["How many hours does it take a man to eat a Helicopter?"],
+        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
+    ],
+)
+
+
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+
+    chat_interface.render()
+    gr.Markdown(LICENSE)
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(share=False)

style.css

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+h1 {
+    text-align: center;
+  }
+  
+  #duplicate-button {
+    margin: auto;
+    color: white;
+    background: #1565c0;
+    border-radius: 100vh;
+  }
+  
+  .contain {
+    max-width: 900px;
+    margin: auto;
+    padding-top: 1.5rem;
+  }