chore: use local as app_file

README.md

@@ -5,7 +5,7 @@ colorFrom: blue
 colorTo: blue
 sdk: gradio
 sdk_version: 5.7.1
-app_file: app.py
+app_file: local.py
 pinned: false
 short_description: Reason about papers using LLMs
 license: agpl-3.0

app.py

@@ -1,207 +0,0 @@
-import os
-import time
-from typing import Dict, List, Optional, TypeAlias
-
-import gradio as gr
-import torch
-import weave
-from papersai.utils import load_paper_as_context
-from transformers import pipeline
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-HistoryType: TypeAlias = List[Dict[str, str]]
-
-# Initialize the LLM and Weave client
-client = weave.init("papersai")
-checkpoint: str = "HuggingFaceTB/SmolLM2-135M-Instruct"
-pipe = pipeline(
-    model=checkpoint,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-
-
-class ChatState:
-    """Utility class to store context and last response"""
-
-    def __init__(self):
-        self.context = None
-        self.last_response = None
-
-
-def record_feedback(x: gr.LikeData) -> None:
-    """
-    Logs user feedback on the assistant's response in the form of a
-    like/dislike reaction.
-
-    Reference:
-    * https://weave-docs.wandb.ai/guides/tracking/feedback
-
-    Args:
-        x (gr.LikeData): User feedback data
-
-    Returns:
-        None
-    """
-    call = state.last_response
-
-    # Remove any existing feedback before adding new feedback
-    for existing_feedback in list(call.feedback):
-        call.feedback.purge(existing_feedback.id)
-
-    if x.liked:
-        call.feedback.add_reaction("👍")
-    else:
-        call.feedback.add_reaction("👎")
-
-
-@weave.op()
-def invoke(history: HistoryType):
-    """
-    Simple wrapper around llm inference wrapped in a weave op
-
-    Args:
-        history (HistoryType): Chat history
-
-    Returns:
-        BaseMessage: Response from the model
-    """
-    input_text = pipe.tokenizer.apply_chat_template(
-        history,
-        tokenize=False,
-    )
-    response = pipe(input_text, do_sample=True, top_p=0.95, max_new_tokens=100)[0][
-        "generated_text"
-    ]
-    response = response.split("\nassistant\n")[-1]
-    return response
-
-
-def update_state(history: HistoryType, message: Optional[Dict[str, str]]):
-    """
-    Update history and app state with the latest user input.
-
-    Args:
-        history (HistoryType): Chat history
-        message (Optional[Dict[str, str]]): User input message
-
-    Returns:
-        Tuple[HistoryType, gr.MultimodalTextbox]: Updated history and chat input
-    """
-    if message is None:
-        return history, gr.MultimodalTextbox(value=None, interactive=True)
-
-    # Initialize history if None
-    if history is None:
-        history = []
-
-    # Handle file uploads without adding to visible history
-    if isinstance(message, dict) and "files" in message:
-        for file_path in message["files"]:
-            try:
-                text = load_paper_as_context(file_path=file_path)
-                doc_context = [x.get_content() for x in text]
-                state.context = " ".join(doc_context)[
-                    : pipe.model.config.max_position_embeddings
-                ]
-                history.append(
-                    {"role": "system", "content": f"Context: {state.context}\n"}
-                )
-            except Exception as e:
-                history.append(
-                    {"role": "assistant", "content": f"Error loading file: {str(e)}"}
-                )
-
-    # Handle text input
-    if isinstance(message, dict) and message.get("text"):
-        history.append({"role": "user", "content": message["text"]})
-
-    return history, gr.MultimodalTextbox(value=None, interactive=True)
-
-
-def bot(history: HistoryType):
-    """
-    Generate response from the LLM and stream it back to the user.
-
-    Args:
-        history (HistoryType): Chat history
-
-    Yields:
-        response from the LLM
-    """
-    if not history:
-        return history
-
-    try:
-        # Get response from LLM
-        response, call = invoke.call(history)
-        state.last_response = call
-
-        # Add empty assistant message
-        history.append({"role": "assistant", "content": ""})
-
-        # Stream the response
-        for character in response:
-            history[-1]["content"] += character
-            time.sleep(0.02)
-            yield history
-
-    except Exception as e:
-        history.append({"role": "assistant", "content": f"Error: {str(e)}"})
-        yield history
-
-
-def create_interface():
-    with gr.Blocks() as demo:
-        global state
-        state = ChatState()
-        gr.Markdown(
-            """
-            <a href="https://github.com/SauravMaheshkar/papersai">
-                <div align="center"><h1>papers.ai</h1></div>
-            </a>
-            """,
-        )
-        chatbot = gr.Chatbot(
-            show_label=False,
-            height=600,
-            type="messages",
-            show_copy_all_button=True,
-            placeholder="Upload a research paper and ask questions!!",
-        )
-
-        chat_input = gr.MultimodalTextbox(
-            interactive=True,
-            file_count="single",
-            placeholder="Upload a document or type your message...",
-            show_label=False,
-        )
-
-        chat_msg = chat_input.submit(
-            fn=update_state,
-            inputs=[chatbot, chat_input],
-            outputs=[chatbot, chat_input],
-        )
-
-        bot_msg = chat_msg.then(  # noqa: F841
-            fn=bot, inputs=[chatbot], outputs=chatbot, api_name="bot_response"
-        )
-
-        chatbot.like(
-            fn=record_feedback,
-            inputs=None,
-            outputs=None,
-            like_user_message=True,
-        )
-
-    return demo
-
-
-def main():
-    demo = create_interface()
-    demo.launch(share=False)
-
-
-if __name__ == "__main__":
-    main()