# 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 """

This app has moved.

Please click the link below to visit the new version:

👉 Go to the new app
""" demo = gr.Interface(fn=show_link, inputs=[], outputs=gr.HTML()) demo.launch()