Update app.py

app.py

@@ -1,43 +1,40 @@
 import os
-import subprocess
-import sys
-
-def install(package):
-    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
-
-# Install torch first
-install("torch")
-
-# Install flash_attn next
-install("flash_attn")
-
-# Now install other dependencies
-install("huggingface_hub==0.22.2")
-install("transformers")
-install("openai")
-install("gradio")
-install("einops")
-install("timm")
+os.system('pip install transformers')
+os.system('pip install datasets')
+os.system('pip install gradio')
+os.system('pip install minijinja')
 
 import gradio as gr
 from huggingface_hub import InferenceClient
-from transformers import AutoModelForCausalLM, pipeline
+from transformers import pipeline
+from datasets import load_dataset
 
-# Use a pipeline as a high-level helper
-pipe = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa", trust_remote_code=True)
+dataset = load_dataset("ibunescu/qa_legal_dataset_train")
 
-# Load model directly
-model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)
+# 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(message, history, system_message, max_tokens, temperature, top_p):
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
     messages = [{"role": "system", "content": system_message}]
+
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
+
     messages.append({"role": "user", "content": message})
 
     response = ""
@@ -49,45 +46,167 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-        response += token
-        yield response
-
-def process_video(video):
-    return f"Processing video: {video.name}"
-
-def process_pdf(pdf):
-    return f"Processing PDF: {pdf.name}"
-
-def process_image(image):
-    return f"Processing image: {image.name}"
-
-video_upload = gr.Interface(fn=process_video, inputs=gr.Video(), outputs="text", title="Upload a Video")
-pdf_upload = gr.Interface(fn=process_pdf, inputs=gr.File(file_types=['.pdf']), outputs="text", title="Upload a PDF")
-image_upload = gr.Interface(fn=process_image, inputs=gr.Image(), outputs="text", title="Upload an Image")
-
-tabbed_interface = gr.TabbedInterface([video_upload, pdf_upload, image_upload], ["Video", "PDF", "Image"])
-
-demo = gr.Blocks()
-
-with demo:
-    with gr.Tab("Chat Interface"):
-        gr.ChatInterface(
-            respond,
-            additional_inputs=[
-                gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-                gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-                gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-                gr.Slider(
-                    minimum=0.1,
-                    maximum=1.0,
-                    value=0.95,
-                    step=0.05,
-                    label="Top-p (nucleus sampling)",
-                ),
-            ],
-        )
-    with gr.Tab("Upload Files"):
-        tabbed_interface
-
+        if token is not None:
+            response += token
+        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
+
+    # 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:
+        return "yellow"
+    else:
+        return "red"
+
+# Custom CSS for white background and black text for input and output boxes
+custom_css = """
+body {
+    background-color: #ffffff;
+    color: #000000;
+    font-family: Arial, sans-serif;
+}
+.gradio-container {
+    max-width: 1000px;
+    margin: 0 auto;
+    padding: 20px;
+    background-color: #ffffff;
+    border: 1px solid #e0e0e0;
+    border-radius: 8px;
+    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
+}
+.gr-button {
+    background-color: #ffffff !important;
+    border-color: #ffffff !important;
+    color: #000000 !important;
+}
+.gr-button:hover {
+    background-color: #ffffff !important;
+    border-color: #004085 !important;
+}
+.gr-input, .gr-textbox, .gr-slider, .gr-markdown, .gr-chatbox {
+    border-radius: 4px;
+    border: 1px solid #ced4da;
+    background-color: #ffffff !important;
+    color: #000000 !important;
+}
+.gr-input:focus, .gr-textbox:focus, .gr-slider:focus {
+    border-color: #ffffff;
+    outline: 0;
+    box-shadow: 0 0 0 0.2rem rgba(255, 255, 255, 1.0);
+}
+#flagging-button {
+    display: none;
+}
+footer {
+    display: none;
+}
+.chatbox .chat-container .chat-message {
+    background-color: #ffffff !important;
+    color: #000000 !important;
+}
+.chatbox .chat-container .chat-message-input {
+    background-color: #ffffff !important;
+    color: #000000 !important;
+}
+.gr-markdown {
+    background-color: #ffffff !important;
+    color: #000000 !important;
+}
+.gr-markdown h1, .gr-markdown h2, .gr-markdown h3, .gr-markdown h4, .gr-markdown h5, .gr-markdown h6, .gr-markdown p, .gr-markdown ul, .gr-markdown ol, .gr-markdown li {
+    color: #000000 !important;
+}
+.score-box {
+    width: 60px;
+    height: 60px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 12px;
+    font-weight: bold;
+    color: black;
+    margin: 5px;
+}
+"""
+
+# Function to facilitate the conversation between the two chatbots
+def chat_between_bots(system_message1, system_message2, max_tokens, temperature, top_p, history1, history2, shared_history, message):
+    response1, history1 = list(respond(message, history1, system_message1, max_tokens, temperature, top_p))[-1]
+    response2, history2 = list(respond(message, history2, system_message2, max_tokens, temperature, top_p))[-1]
+    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)
+    
+    prosecutor_color = color_code(score1)
+    defense_color = color_code(score2)
+    
+    prosecutor_score_color = f"<div class='score-box' style='background-color:{prosecutor_color};'>Score: {score1}</div>"
+    defense_score_color = f"<div class='score-box' style='background-color:{defense_color};'>Score: {score2}</div>"
+    
+    return response1, response2, history1, history2, shared_history, f"{response1}\n\n{response2}", prosecutor_score_color, defense_score_color
+
+# Gradio interface
+with gr.Blocks(css=custom_css) as demo:
+    history1 = gr.State([])
+    history2 = gr.State([])
+    shared_history = gr.State([])
+    message = gr.Textbox(label="Shared Input Box")
+    system_message1 = gr.State("You are an expert at legal Prosecution.")
+    system_message2 = gr.State("You are an expert at legal Defense.")
+    max_tokens = gr.State(512)  # Adjusted to balance response length
+    temperature = gr.State(0.7)
+    top_p = gr.State(0.95)
+    
+    with gr.Row():
+        with gr.Column(scale=4):
+            prosecutor_response = gr.Textbox(label="Prosecutor's Response", interactive=False)
+        with gr.Column(scale=1):
+            prosecutor_score_color = gr.HTML()
+            
+    with gr.Row():
+        with gr.Column(scale=4):
+            defense_response = gr.Textbox(label="Defense Attorney's Response", interactive=False)
+        with gr.Column(scale=1):
+            defense_score_color = gr.HTML()
+    
+    shared_argument = gr.Textbox(label="Shared Argument", interactive=False)
+    
+    submit_btn = gr.Button("Submit")
+
+    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])
+    
 if __name__ == "__main__":
     demo.launch()