Update app.py

app.py

@@ -15,9 +15,6 @@ dataset = load_dataset("ibunescu/qa_legal_dataset_train")
 # Use a pipeline as a high-level helper
 pipe = pipeline("fill-mask", model="nlpaueb/legal-bert-base-uncased")
 
-"""
-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(
@@ -52,32 +49,27 @@ def respond(
         yield response, history + [(message, response)]
 
 def score_argument(argument):
-    # Keywords related to legal arguments
     merits_keywords = ["compelling", "convincing", "strong", "solid"]
     laws_keywords = ["statute", "law", "regulation", "act"]
     precedents_keywords = ["precedent", "case", "ruling", "decision"]
     verdict_keywords = ["guilty", "innocent", "verdict", "judgment"]
 
-    # Initialize scores
     merits_score = sum([1 for word in merits_keywords if word in argument.lower()])
     laws_score = sum([1 for word in laws_keywords if word in argument.lower()])
     precedents_score = sum([1 for word in precedents_keywords if word in argument.lower()])
     verdict_score = sum([1 for word in verdict_keywords if word in argument.lower()])
     length_score = len(argument.split())
 
-    # Additional evaluations for legal standards
-    merits_value = merits_score * 2  # Each keyword in merits is valued at 2 points
-    laws_value = laws_score * 3      # Each keyword in laws is valued at 3 points
-    precedents_value = precedents_score * 4  # Each keyword in precedents is valued at 4 points
-    verdict_value = verdict_score * 5  # Each keyword in verdict is valued at 5 points
+    merits_value = merits_score * 2
+    laws_value = laws_score * 3
+    precedents_value = precedents_score * 4
+    verdict_value = verdict_score * 5
 
-    # Total score: Sum of all individual scores
     total_score = merits_value + laws_value + precedents_value + verdict_value + length_score
 
     return total_score
 
 def color_code(score):
-    # Green for high score, yellow for medium, red for low
     if score > 50:
         return "green"
     elif score > 30:
@@ -153,6 +145,13 @@ footer {
     color: black;
     margin: 5px;
 }
+.scroll-box {
+    max-height: 200px;
+    overflow-y: scroll;
+    border: 1px solid #ced4da;
+    padding: 10px;
+    border-radius: 4px;
+}
 """
 
 # Function to facilitate the conversation between the two chatbots
@@ -162,12 +161,10 @@ def chat_between_bots(system_message1, system_message2, max_tokens, temperature,
     shared_history.append(f"Prosecutor: {response1}")
     shared_history.append(f"Defense Attorney: {response2}")
     
-    # Ensure the responses are balanced by limiting the length
     max_length = max(len(response1), len(response2))
     response1 = response1[:max_length]
     response2 = response2[:max_length]
     
-    # Calculate scores and scoring matrices
     score1 = score_argument(response1)
     score2 = score_argument(response2)
     
@@ -179,11 +176,9 @@ def chat_between_bots(system_message1, system_message2, max_tokens, temperature,
     
     return response1, response2, history1, history2, shared_history, f"{response1}\n\n{response2}", prosecutor_score_color, defense_score_color
 
-# Function to handle PDF uploads and display them in a gallery
 def update_pdf_gallery(pdf_files):
     return pdf_files
 
-# Function to add message to the chatbot
 def add_message(history, message):
     for x in message["files"]:
         history.append(((x,), None))
@@ -191,7 +186,6 @@ def add_message(history, message):
         history.append((message["text"], None))
     return history, gr.MultimodalTextbox(value=None, interactive=False)
 
-# Bot function to simulate response
 def bot(history):
     response = "**That's cool!**"
     history[-1][1] = ""
@@ -200,7 +194,6 @@ def bot(history):
         time.sleep(0.05)
         yield history
 
-# Function to handle likes and dislikes
 def print_like_dislike(x: gr.LikeData):
     print(x.index, x.value, x.liked)
 
@@ -210,48 +203,53 @@ with gr.Blocks(css=custom_css) as demo:
     shared_history = gr.State([])
     pdf_files = gr.State([])
     
-    message = gr.Textbox(label="Case to Argue")
-    system_message1 = gr.State("You are an expert Prosecutor. Give your best arguments for the case on behalf of the prosecution.")
-    system_message2 = gr.State("You are an expert Defense Attorney. Give your best arguments for the case on behalf of the Defense.")
-    max_tokens = gr.State(512)  # Adjusted to balance response length
-    temperature = gr.State(0.6)
-    top_p = gr.State(0.95)
-    
-    with gr.Row():
-        with gr.Column(scale=4):
-            prosecutor_response = gr.Textbox(label="Prosecutor's Response", interactive=True)
-        with gr.Column(scale=1):
-            prosecutor_score_color = gr.HTML()
+    with gr.Tab("Argument Evaluation"):
+        message = gr.Textbox(label="Case to Argue")
+        system_message1 = gr.State("You are an expert Prosecutor. Give your best arguments for the case on behalf of the prosecution.")
+        system_message2 = gr.State("You are an expert Defense Attorney. Give your best arguments for the case on behalf of the Defense.")
+        max_tokens = gr.State(512)
+        temperature = gr.State(0.6)
+        top_p = gr.State(0.95)
         
-        with gr.Column(scale=4):
-            defense_response = gr.Textbox(label="Defense Attorney's Response", interactive=True)
-        with gr.Column(scale=1):
-            defense_score_color = gr.HTML()
-    
-    shared_argument = gr.Textbox(label="Case Outcome", interactive=True)
-    pdf_upload = gr.File(label="Upload Case Files (PDF)", file_types=[".pdf"])
-    pdf_gallery = gr.Gallery(label="PDF Gallery")
+        with gr.Row():
+            with gr.Column(scale=4):
+                prosecutor_response = gr.Textbox(label="Prosecutor's Response", interactive=True)
+                prosecutor_response.style(container=True).add_class("scroll-box")
+            with gr.Column(scale=1):
+                prosecutor_score_color = gr.HTML()
+            
+            with gr.Column(scale=4):
+                defense_response = gr.Textbox(label="Defense Attorney's Response", interactive=True)
+                defense_response.style(container=True).add_class("scroll-box")
+            with gr.Column(scale=1):
+                defense_score_color = gr.HTML()
+        
+        shared_argument = gr.Textbox(label="Case Outcome", interactive=True)
+        submit_btn = gr.Button("Argue")
+        
+        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_history, shared_argument, prosecutor_score_color, defense_score_color])
     
-    submit_btn = gr.Button("Argue")
-    pdf_upload_btn = gr.Button("Update PDF Gallery")
-
-    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_history, shared_argument, prosecutor_score_color, defense_score_color])
-    pdf_upload_btn.click(update_pdf_gallery, inputs=[pdf_upload], outputs=[pdf_gallery, pdf_files])
+    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_upload_btn = gr.Button("Update PDF Gallery")
+        
+        pdf_upload_btn.click(update_pdf_gallery, inputs=[pdf_upload], outputs=[pdf_gallery, pdf_files])
     
-    # Chatbot section
-    chatbot = gr.Chatbot(
-        [],
-        elem_id="chatbot",
-        bubble_full_width=False
-    )
+    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_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, chatbot, chatbot, api_name="bot_response")
-    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
+        chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
+        bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
+        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
 
-    chatbot.like(print_like_dislike, None, None)
+        chatbot.like(print_like_dislike, None, None)
 
 demo.queue()
 demo.launch()