Update app.py

app.py

@@ -1,73 +1,57 @@
-import os
 import gradio as gr
-import faiss
-import numpy as np
-import pickle
-from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer, AutoModelForCausalLM
 
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-if not HF_TOKEN:
-    raise ValueError("HF_TOKEN environment variable not set. Please configure it in Space settings.")
-
-
-# Load precomputed chunks and FAISS index
-with open("chunks.pkl", "rb") as f:
-    chunks = pickle.load(f)
-index = faiss.read_index("index.faiss")
-
-# Load embedding model (same as used in preprocessing)
-embedding_model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
-
-
-# Load Jais model and tokenizer
-model_name = "aubmindlab/aragpt2-base"
-tokenizer = AutoTokenizer.from_pretrained(model_name, token=HF_TOKEN, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(model_name, token=HF_TOKEN, trust_remote_code=True)
-
-
-# RAG function to retrieve and generate a response
-def get_response(query, k=3):
-    query_embedding = embedding_model.encode([query])
-    distances, indices = index.search(np.array(query_embedding), k)
-    retrieved_chunks = [chunks[i] for i in indices[0]]
-    context = " ".join(retrieved_chunks)
-    prompt = f"Based on the following documents: {context}, answer the question: {query}"
-    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=200,
-        do_sample=True,
-        temperature=0.7,
-        top_p=0.9,
-        return_full_text=False
-    )
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response.split(query)[-1].strip()
-
-# Gradio interface
-import gradio as gr
+# Placeholder response function
+def get_response(user_message):
+    return f"رد تلقائي: {user_message}"  # Replace this with your AI model
 
 with gr.Blocks(title="المتحدث الآلي للتشريعات المحلية لإمارة دبي") as demo:
-    gr.Markdown("# Dubai Legislation Chatbot\nاسأل أي سؤال حول تشريعات دبي- نسخة تجريبة (تصميم وتنفيذ م.أسامة الخطيب")
-    chatbot = gr.Chatbot(elem_id="chatbot")  # Assign an ID for CSS targeting
-    msg = gr.Textbox(placeholder="اكتب سؤالك هنا...", rtl=True)
-    clear = gr.Button("مسح")
+    gr.Markdown("# Dubai Legislation Chatbot\nاسأل أي سؤال حول تشريعات دبي - نسخة تجريبية (تصميم وتنفيذ م. أسامة الخطيب)", elem_id="title")
+
+    chatbot = gr.Chatbot(elem_id="chatbot")  
+    msg = gr.Textbox(placeholder="اكتب سؤالك هنا...", rtl=True, elem_id="input-box")
+    clear = gr.Button("مسح", elem_id="clear-btn")
 
     def user(user_message, history):
-        return "", history + [[user_message, None]]
+        history.append([user_message, None])
+        return "", history
 
     def bot(history):
         user_message = history[-1][0]
-        bot_message = get_response(user_message)  # Your response function
+        bot_message = get_response(user_message)  
         history[-1][1] = bot_message
         return history
 
     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
         bot, chatbot, chatbot
     )
-    clear.click(lambda: None, None, chatbot, queue=False)
 
-# Launch with custom CSS
-demo.launch()
+    clear.click(lambda: [], None, chatbot, queue=False)
+
+# Launch with custom RTL-friendly CSS
+demo.launch(css="""
+#title {
+    text-align: center;
+    font-size: 24px;
+    color: darkblue;
+    direction: rtl;
+}
+#chatbot {
+    background-color: #f9f9f9;
+    border-radius: 10px;
+    padding: 10px;
+    direction: rtl;
+    text-align: right;
+    font-family: 'Tajawal', sans-serif;
+}
+#input-box {
+    border: 2px solid blue;
+    padding: 10px;
+    direction: rtl;
+    text-align: right;
+}
+#clear-btn {
+    background-color: red;
+    color: white;
+    font-weight: bold;
+}
+""")