Update app.py

app.py

@@ -11,6 +11,7 @@ from transformers import pipeline
 from datasets import load_dataset
 import fitz  # PyMuPDF
 
+# Loading datasets
 dataset = load_dataset("ibunescu/qa_legal_dataset_train")
 
 # Different pipelines for different tasks
@@ -18,6 +19,7 @@ qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2"
 summarization_pipeline = pipeline("summarization", model="facebook/bart-large-cnn")
 mask_filling_pipeline = pipeline("fill-mask", model="nlpaueb/legal-bert-base-uncased")
 
+# Inference client for chat completion
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 def respond(
@@ -69,7 +71,7 @@ def generate_case_outcome(prosecutor_response, defense_response):
 
 def determine_winner(outcome):
     if "Prosecutor" in outcome and "Defense" in outcome:
-        if "Prosecutor" in outcome.split().count("Prosecutor") > outcome.split().count("Defense"):
+        if outcome.count("Prosecutor") > outcome.count("Defense"):
             return "Prosecutor Wins"
         else:
             return "Defense Wins"
@@ -192,7 +194,7 @@ def update_pdf_gallery_and_extract_text(pdf_files):
     return pdf_files, pdf_text
 
 def get_top_10_cases():
-    result = summarization_pipeline("Top 10 current legal cases in the country", max_length=150, min_length=50, do_sample=False)
+    result = summarization_pipeline("Show 10 legal case with names and numbers from any State Court of Appeals in the US.", max_length=150, min_length=50, do_sample=False)
     return result[0]['summary_text']
 
 def add_message(history, message):
@@ -281,6 +283,14 @@ with gr.Blocks(css=custom_css) as demo:
                 submit_btn.click(chat_between_bots, inputs=[system_message1, system_message2, max_tokens, temperature, top_p, history1, history2, shared_history, message], outputs=[prosecutor_response, defense_response, history1, history2, shared_argument, winner])
                 clear_btn.click(reset_conversation, outputs=[history1, history2, shared_history, prosecutor_response, defense_response, shared_argument, winner])
                 save_btn.click(save_conversation, inputs=[history1, history2, shared_history], outputs=[history1, history2, shared_history])
+                
+                # Inner HTML for asking about the case outcome
+                with gr.Row():
+                    case_question = gr.Textbox(label="Ask a Question about the Case Outcome")
+                    case_answer = gr.Textbox(label="Answer", interactive=False, elem_classes=["scroll-box"])
+                    ask_case_btn = gr.Button("Ask")
+                    
+                    ask_case_btn.click(ask_about_case_outcome, inputs=[shared_history, case_question], outputs=case_answer)
     
     with gr.Tab("PDF Management"):
         pdf_upload = gr.File(label="Upload Case Files (PDF)", file_types=[".pdf"])
@@ -309,13 +319,6 @@ with gr.Blocks(css=custom_css) as demo:
         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"])
-        ask_case_btn = gr.Button("Ask")
-
-        ask_case_btn.click(ask_about_case_outcome, inputs=[shared_history, case_question], outputs=case_answer)
 
 demo.queue()
 demo.launch()