using fastapi

README.md

@@ -5,7 +5,7 @@ colorFrom: blue
 colorTo: red
 sdk: gradio
 sdk_version: 3.50.2
-app_file: app.py
+app_file: start.py
 pinned: false
 license: apache-2.0
 ---

app.py

@@ -1,7 +1,5 @@
-import io
 import os
 import re
-import tarfile
 
 from anthropic import AI_PROMPT, HUMAN_PROMPT, Anthropic
 import gradio as gr
@@ -11,7 +9,12 @@ import arxiv
 from arxiv_latex_extractor import get_paper_content
 import requests
 
-LEADING_PROMPT = "Read the following paper and answer the question below:"
+from coreservice import app
+from fastapi.staticfiles import StaticFiles
+
+
+LEADING_PROMPT = "Read the following paper:"
+
 
 
 def replace_texttt(text):
@@ -72,10 +75,6 @@ class ContextualQA:
 
         prompt = f"{HUMAN_PROMPT}  {full_context} {HUMAN_PROMPT} {question} {AI_PROMPT}"
 
-        # save prompt on disk for examination
-        with open("prompt.txt", "w") as f:
-            f.write(prompt)
-
         response = self.client.completions.create(
             prompt=prompt,
             stop_sequences=[HUMAN_PROMPT],
@@ -121,9 +120,7 @@ def load_context(paper_id):
     context = f"{LEADING_PROMPT}\n{latex_source}"
     qa_model.load_text(context)
 
-    # Usage
     title, abstract = get_paper_info(paper_id)
-    # remove special symbols from title and abstract
     title = replace_texttt(title)
     abstract = replace_texttt(abstract)
 
@@ -193,7 +190,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     with gr.Column():
         with gr.Row():
-            paper_id_input = gr.Textbox(label="Enter Paper ID", value="2108.07258")
+            paper_id_input = gr.Textbox(label="Enter Paper ID", value="2310.12103")
             btn_load = gr.Button("Load Paper")
             qa_model = gr.State()
 
@@ -213,7 +210,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         "## Acknowledgements\n"
         "This project is made possible through the generous support of "
-        "[Anthropic](https://www.anthropic.com/), who provided free access to the `Claude-2.0` API."
+        "[Anthropic](https://www.anthropic.com/), who provided free access to the `claude-2.0` API."
     )
 
     btn_load.click(load_context, inputs=[paper_id_input], outputs=[qa_model, chatbot])
@@ -232,4 +229,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     btn_clear.click(clear_context, outputs=[chatbot])
 
-demo.launch()
+# demo.launch()
+
+app.mount("/js", StaticFiles(directory="js"), name="js")
+gr.mount_gradio_app(app, demo, path="/")

coreservice.py

@@ -0,0 +1,48 @@
+from fastapi import FastAPI, Request, Response
+
+filenames = ["js/extra.js"]
+contents = "\n".join(
+    [f"<script type='text/javascript' src='{x}'></script>" for x in filenames]
+)
+
+ga_script = """
+<!-- Google tag (gtag.js) -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-EZ77X5T529"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-EZ77X5T529');
+</script>
+"""
+
+app = FastAPI()
+
+
+@app.middleware("http")
+async def insert_js(request: Request, call_next):
+    path = request.scope["path"]  # get the request route
+    response = await call_next(request)
+
+    if path == "/":
+        response_body = ""
+        async for chunk in response.body_iterator:
+            response_body += chunk.decode()
+
+        charset_tag = '<meta charset="utf-8" />'
+        if charset_tag in response_body:
+            response_body = response_body.replace(charset_tag, charset_tag + ga_script)
+
+        response_body = response_body.replace("</body>", contents + "</body>")
+
+        del response.headers["content-length"]
+
+        return Response(
+            content=response_body,
+            status_code=response.status_code,
+            headers=dict(response.headers),
+            media_type=response.media_type,
+        )
+
+    return response

requirements.txt

@@ -7,4 +7,5 @@ tqdm
 numpy
 arxiv
 tiktoken
-git+https://github.com/taesiri/arxiv_latex_extractor
+git+https://github.com/taesiri/arxiv_latex_extractor
+fastapi

start.py

@@ -0,0 +1,3 @@
+import subprocess
+
+subprocess.run("uvicorn app:app --host 0.0.0.0 --port 7860", shell=True)