test

app.py

@@ -1,63 +1,28 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def generate(paper_text):
+    return "Success"
 
-    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 = ""
+with gr.Blocks() as demo:
+    title = gr.Markdown(title)
+    steps = gr.Markdown(steps)
+    instr = gr.Markdown("## Upload your paper as a pdf file")
+    file_input = gr.File(file_types=[".pdf"], file_count="single")
+    markdown_field = gr.Textbox(label="Markdown", max_lines=20, autoscroll=False)
+    # generate_button = gr.Button("Generate Review", interactive=not markdown_field)
+    generate_button = gr.Button("Generate Review")
+    file_input.upload(process_file, file_input, markdown_field)
+    # markdown_field.change(lambda text: gr.update(interactive=True) if len(text) > 1000 else gr.update(interactive=False), markdown_field, generate_button)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    review_field = gr.Markdown(label="Review")
+    # generate_button.click(fn=lambda: gr.update(interactive=False), inputs=None, outputs=generate_button).then(generate, markdown_field, review_field).then(fn=lambda: gr.update(interactive=True), inputs=None, outputs=generate_button)
+    generate_button.click(fn=lambda: gr.update(interactive=False), inputs=None, outputs=generate_button).then(generate, markdown_field, review_field).then(fn=lambda: gr.update(interactive=True), inputs=None, outputs=generate_button)
+    demo.title = "Paper Review Generator"
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    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=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
 
 if __name__ == "__main__":