| # import os | |
| # import sys | |
| # sys.path.append(os.path.join(os.path.dirname(__file__), "src")) | |
| # import tempfile | |
| # from pathlib import Path | |
| # import base64 | |
| # import subprocess | |
| # import json | |
| # import gradio as gr | |
| # import requests | |
| # from src.custom_main import generate_diagram_demo | |
| # # Import your project's modules here | |
| # # from diagram_generator import generate_diagram | |
| # # from paper_processor import process_paper | |
| # def process_pdf(pdf_file) -> str: | |
| # """Extract text from PDF file using local API service""" | |
| # print(f"====> Processing PDF: {pdf_file.name}") | |
| # try: | |
| # with open(pdf_file.name, "rb") as f: | |
| # files = {"file": (pdf_file.name, f, "application/pdf")} | |
| # response = requests.post( | |
| # "https://amusing-presently-chow.ngrok-free.app/parse-pdf", | |
| # files=files | |
| # ) | |
| # # Call local API service to process PDF | |
| # # response = requests.post( | |
| # # "http://amusing-presently-chow.ngrok-free.app/parse-pdf", | |
| # # json={"pdf_path": pdf_file} | |
| # # ) | |
| # # Check if the request was successful | |
| # response.raise_for_status() | |
| # # Parse the JSON response | |
| # output = response.json() | |
| # return output | |
| # except requests.RequestException as e: | |
| # print(f"Error calling PDF processing API: {e}") | |
| # return f"Error calling PDF processing API: {str(e)}" | |
| # except json.JSONDecodeError as e: | |
| # print(f"Error parsing API response: {e}") | |
| # return f"Error parsing API response: {response.text}" | |
| # except Exception as e: | |
| # print(f"Unexpected error: {e}") | |
| # return f"Unexpected error: {str(e)}" | |
| # def encode_image(image_path: str) -> str: | |
| # """Encode image to base64 string""" | |
| # with open(image_path, "rb") as image_file: | |
| # return base64.b64encode(image_file.read()).decode('utf-8') | |
| # def create_demo(): | |
| # """Create and launch the Gradio demo interface""" | |
| # with gr.Blocks(title="SCISKETCH Demo") as demo: | |
| # gr.Markdown("SCISKETCH Demo") | |
| # gr.Markdown("Upload a PDF, provide a caption, and optionally upload images to generate a diagram.") | |
| # # State variable to store PDF processing result | |
| # pdf_text_state = gr.State("") | |
| # with gr.Row(): | |
| # with gr.Column(scale=1): | |
| # pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"]) | |
| # process_pdf_btn = gr.Button("Process PDF", variant="secondary") | |
| # caption_input = gr.Textbox(label="Caption", placeholder="Enter a caption for the diagram...") | |
| # image_input = gr.File(label="Upload Images (Optional)", file_types=[".png", ".jpg", ".jpeg"], file_count="multiple") | |
| # submit_btn = gr.Button("Generate Diagram", variant="primary") | |
| # with gr.Column(scale=1): | |
| # pdf_text_display = gr.Textbox(label="PDF Processing Result", lines=10) | |
| # output_display = gr.Textbox(label="Final Result", lines=20) | |
| # # Process PDF button handler | |
| # def process_pdf_handler(pdf_file): | |
| # if pdf_file is None: | |
| # return "No PDF file uploaded", "" | |
| # pdf_text = process_pdf(pdf_file) | |
| # # Format the dictionary output for better display | |
| # if isinstance(pdf_text, dict): | |
| # formatted_output = "## Paper Information\n\n" | |
| # # Display title and authors if available | |
| # if "title" in pdf_text and pdf_text["title"]: | |
| # formatted_output += f"**Title:** {pdf_text['title']}\n\n" | |
| # if "authors" in pdf_text and pdf_text["authors"]: | |
| # formatted_output += f"**Authors:** {pdf_text['authors']}\n\n" | |
| # if "pub_date" in pdf_text and pdf_text["pub_date"]: | |
| # formatted_output += f"**Publication Date:** {pdf_text['pub_date']}\n\n" | |
| # # Display abstract if available | |
| # if "abstract" in pdf_text and pdf_text["abstract"]: | |
| # formatted_output += f"**Abstract:**\n{pdf_text['abstract']}\n\n" | |
| # # Display section count if available | |
| # if "sections" in pdf_text and isinstance(pdf_text["sections"], list): | |
| # formatted_output += f"**Sections:** {len(pdf_text['sections'])}\n\n" | |
| # # Display section headings | |
| # formatted_output += "**Section Headings:**\n" | |
| # for i, section in enumerate(pdf_text["sections"]): | |
| # if "heading" in section and section["heading"]: | |
| # formatted_output += f"{i+1}. {section['heading']}\n" | |
| # # Display reference count if available | |
| # if "references" in pdf_text and isinstance(pdf_text["references"], list): | |
| # formatted_output += f"\n**References:** {len(pdf_text['references'])}\n" | |
| # # Keep the raw JSON for the state | |
| # return formatted_output, pdf_text | |
| # else: | |
| # # If it's not a dictionary, return as is | |
| # return str(pdf_text), pdf_text | |
| # process_pdf_btn.click( | |
| # fn=process_pdf_handler, | |
| # inputs=[pdf_input], | |
| # outputs=[pdf_text_display, pdf_text_state] | |
| # ) | |
| # # Generate diagram button handler | |
| # def analyze_with_pdf_text(pdf_file, pdf_text, caption, image_files): | |
| # print(f"Analyzing with PDF_file {pdf_file}, caption {caption}, image_files {image_files}") | |
| # result = generate_diagram_demo(pdf_text, caption, image_files) | |
| # # # Use the processed PDF text | |
| # # if pdf_text: | |
| # # result += f"Using processed PDF text ({len(pdf_text)} characters).\n\n" | |
| # # elif pdf_file is not None: | |
| # # # Process PDF if not already processed | |
| # # pdf_text = process_pdf(pdf_file.name) | |
| # # result += f"Extracted {len(pdf_text)} characters from PDF.\n\n" | |
| # # Process caption | |
| # # if caption: | |
| # # result += f"Caption: {caption}\n\n" | |
| # # Process images if provided | |
| # # if image_files: | |
| # # result += f"Processed {len(image_files)} images:\n" | |
| # # for i, img in enumerate(image_files): | |
| # # result += f"- Image {i+1}: {Path(img.name).name}\n" | |
| # # Here you would call your actual diagram generation code | |
| # # result = generate_diagram(pdf_text, caption, image_files) | |
| # # For demo purposes, return a placeholder | |
| # # result += "\n\nDiagram generation would happen here based on the inputs." | |
| # return result | |
| # submit_btn.click( | |
| # fn=analyze_with_pdf_text, | |
| # inputs=[pdf_input, pdf_text_state, caption_input, image_input], | |
| # outputs=output_display | |
| # ) | |
| # return demo | |
| # if __name__ == "__main__": | |
| # demo = create_demo() | |
| # demo.launch(server_port=7860) | |
| import gradio as gr | |
| def show_link(): | |
| return """ | |
| <div style="text-align: center; font-family: sans-serif; padding: 40px;"> | |
| <h2>This app has moved.</h2> | |
| <p>Please click the link below to visit the new version:</p> | |
| <a href="https://amusing-presently-chow.ngrok-free.app/" target="_blank" style="font-size: 18px; color: #1f6feb;"> | |
| 👉 Go to the new app | |
| </a> | |
| </div> | |
| """ | |
| demo = gr.Interface(fn=show_link, inputs=[], outputs=gr.HTML()) | |
| demo.launch() | |