Update app.py

app.py

@@ -1,32 +1,24 @@
+import os
+os.system('pip install transformers')
+os.system('pip install gradio')
+os.system('pip install requests')
+
+import requests
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import pipeline
 
-"""
-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
-"""
+# Inference client for chat completion
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Different pipelines for different tasks
+qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+def respond(message, 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 = ""
-
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -35,29 +27,96 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
+        if token is not None:
+            response += token
+    return response
 
-        response += token
-        yield response
+def generate_defense_argument(case_details):
+    system_message = (
+        "You are an expert Defense Attorney. Provide the best and most detailed arguments "
+        "to defend the case based on the given case details. Include thorough analysis, "
+        "evidence presentation, and any relevant legal precedents."
+    )
+    arguments = respond(case_details, system_message, max_tokens=1024, temperature=0.7, top_p=0.95)
+    return arguments
 
+# Custom CSS for a clean layout
+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;
+    margin: 5px;
+}
+.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
+}
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = 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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+# Gradio Interface
+with gr.Blocks(css=custom_css) as demo:
+    with gr.Column():
+        gr.Markdown("# Defense Expert\n### Provide Case Details")
+        case_details = gr.Textbox(lines=5, placeholder="Enter case details here...")
+        defense_argument = gr.Textbox(lines=10, placeholder="Defense's Argument...")
+        
+        generate_btn = gr.Button("Generate Argument")
+        generate_btn.click(generate_defense_argument, inputs=[case_details], outputs=[defense_argument])
+        
+        clear_btn = gr.Button("Clear")
+        clear_btn.click(lambda: "", None, defense_argument)
+
+demo.launch()