Update app.py

app.py

@@ -12,8 +12,6 @@ from huggingface_hub import InferenceClient
 from transformers import pipeline, AutoTokenizer, AutoModelForMaskedLM
 from datasets import load_dataset
 import fitz  # PyMuPDF
-from pdf2image import convert_from_path
-from gradio_pdf import PDF
 from pathlib import Path
 
 dir_ = Path(__file__).parent
@@ -187,25 +185,6 @@ def chat_between_bots(system_message1, system_message2, max_tokens, temperature,
     
     return response1, response2, history1, history2, shared_history, outcome
 
-def extract_text_from_pdf(pdf_file):
-    text = ""
-    doc = fitz.open(pdf_file)
-    for page in doc:
-        text += page.get_text()
-    return text
-
-def ask_about_pdf(pdf_text, question, history):
-    system_message = "You are a legal expert answering questions based on the PDF content provided."
-    response = list(respond(question, history, system_message, max_tokens=512, temperature=0.6, top_p=0.95))[-1][0]
-    return response
-
-def update_pdf_gallery_and_extract_text(pdf_files):
-    if len(pdf_files) > 0:
-        pdf_text = extract_text_from_pdf(pdf_files[0].name)
-    else:
-        pdf_text = ""
-    return pdf_files, pdf_text
-
 def get_top_10_cases():
     prompt = "List 10 high-profile legal cases that have received significant media attention and are currently ongoing. Just a list of case names and numbers."
     response = ""
@@ -229,11 +208,6 @@ def add_message(history, message):
         history.append((message["text"], None))
     return history, gr.MultimodalTextbox(value=None, interactive=True)
 
-def bot(history, message):
-    system_message = "You are a helpful assistant."
-    response = list(respond(message, history, system_message, max_tokens=150, temperature=0.6, top_p=0.95))[-1][0]
-    return response, history
-
 def print_like_dislike(x: gr.LikeData):
     print(x.index, x.value, x.liked)
 
@@ -259,17 +233,10 @@ def ask_about_case_outcome(shared_history, question):
             response += token
     return response
 
-def qa(question: str, doc: str) -> str:
-    img = convert_from_path(doc)[0]
-    output = pipe(img, question)
-    return sorted(output, key=lambda x: x["score"], reverse=True)[0]['answer']
-
 with gr.Blocks(css=custom_css) as demo:
     history1 = gr.State([])
     history2 = gr.State([])
     shared_history = gr.State([])
-    pdf_files = gr.State([])
-    pdf_text = gr.State("")
     top_10_cases = gr.State("")
 
     with gr.Tab("Argument Evaluation"):
@@ -308,34 +275,6 @@ with gr.Blocks(css=custom_css) as demo:
                 clear_btn.click(reset_conversation, outputs=[history1, history2, shared_history, prosecutor_response, defense_response, outcome])
                 save_btn.click(save_conversation, inputs=[history1, history2, shared_history], outputs=[history1, history2, shared_history])
     
-    with gr.Tab("PDF Management"):
-        pdf_upload = gr.File(label="Upload Case Files (PDF)", file_types=[".pdf"])
-        pdf_gallery = gr.Gallery(label="PDF Gallery")
-        pdf_view = gr.Textbox(label="PDF Content", interactive=False, elem_classes=["scroll-box"])
-        pdf_question = gr.Textbox(label="Ask a Question about the PDF")
-        pdf_answer = gr.Textbox(label="Answer", interactive=False, elem_classes=["scroll-box"])
-        pdf_upload_btn = gr.Button("Update PDF Gallery")
-        pdf_ask_btn = gr.Button("Ask")
-
-        pdf_upload_btn.click(update_pdf_gallery_and_extract_text, inputs=[pdf_upload], outputs=[pdf_gallery, pdf_text])
-        pdf_text.change(fn=lambda x: x, inputs=pdf_text, outputs=pdf_view)
-        pdf_ask_btn.click(qa, inputs=[pdf_question, pdf_text], outputs=pdf_answer)
-    
-    with gr.Tab("Chatbot"):
-        chatbot = gr.Chatbot(
-            [],
-            elem_id="chatbot",
-            bubble_full_width=False
-        )
-
-        chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)
-
-        chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
-        bot_msg = chat_msg.then(bot, inputs=[history1, chat_input], outputs=[chatbot, history1])
-        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
-
-        chatbot.like(print_like_dislike, None, None)
-    
     with gr.Tab("Case Outcome Chat"):
         case_question = gr.Textbox(label="Ask a Question about the Case Outcome")
         case_answer = gr.Textbox(label="Answer", interactive=False, elem_classes=["scroll-box"])