Update app.py

app.py

@@ -5,6 +5,7 @@ import requests
 from weaviate.classes.init import Auth
 from weaviate.classes.query import Rerank
 from transformers import AutoTokenizer, pipeline, AutoModelForSeq2SeqLM
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
 
 
 # ========== إعداد الاتصال مع Weaviate ==========
@@ -52,10 +53,10 @@ def search_for_laws(user_query, client):
 
 # ========== دوال القرار ==========
 def router_decision(queries):
-  results = classifier(queries)
-  labels = [res['label'] for res in results]
-  numeric_labels = [1 if label == 'LABEL_1' else 0 for label in labels]
-  return any(numeric_labels)
+    results = classifier(queries)
+    labels = [res['label'] for res in results]
+    numeric_labels = [1 if label == 'LABEL_1' else 0 for label in labels]
+    return any(numeric_labels)
 
 # ========== LLM responses ==========
 def llm_response_faq(query, docs):
@@ -68,7 +69,9 @@ def llm_response_faq(query, docs):
 
   system_prompt = """
   You are an intelligent assistant specialized in the Jordanian Land and Survey Department. Your task is to provide answers strictly based on the context provided from FAQ files.
+
   Guidelines:
+
   1. Use only the information available in the provided files. Do not hallucinate or invent any information.
   2. If the provided context does not contain a relevant answer to the user's question, respond with: "I do not know the answer."
   3. Correct any spelling or typographical errors present in the extracted text from the files.
@@ -76,7 +79,9 @@ def llm_response_faq(query, docs):
   5. Do not modify the facts or data from the files; respect the sensitivity of the information.
   6. Focus only on questions related to Jordanian land, survey, and administrative data.
   7. Answer in the language of the user's question. Most questions will be in Arabic, so prioritize answering in Arabic when possible.
+
   Instructions for answering:
+
   - First, identify the most relevant FAQ entry based on the user's question.
   - Then, provide the answer exactly as it appears in the file, fixing only spelling mistakes and minor formatting issues.
   - AVOID PROVIDIND INORMATION NOT PRESENT IN THE CONTEXT.
@@ -113,6 +118,7 @@ def llm_response_laws(query, docs):
     system_prompt = """
     You are an intelligent assistant specialized in Jordanian laws and legislation.
     Your task is to provide answers strictly based on the context provided from the legal documents.
+
     Guidelines:
     1. Use only the information available in the provided files. Do not hallucinate.
     2. If the provided context does not contain a relevant answer, respond in Arabic with: "لا أعلم الجواب".
@@ -143,20 +149,20 @@ def llm_response_laws(query, docs):
 
 
 # ========== النظام الرئيسي ==========
-def rag_system(user_input, classifier):
+def rag_system(user_input):
     client = connect_to_db()
-
     queries, faq_docs = search_for_faq(user_input, client)
     use_laws = router_decision(queries)
 
     if use_laws:
         answer = llm_response_faq(user_input, faq_docs)
     else:
-        laws_docs = search_for_laws(user_input, client)
-        answer = llm_response_laws(user_input, laws_docs)
+        law_docs = search_for_laws(user_input, client)
+        answer = llm_response_laws(user_input, law_docs)
+        
 
     client.close()
-    return answer + did_we_get
+    return answer
 
 # ========== واجهة Gradio ==========
 with gr.Blocks() as demo:
@@ -164,8 +170,7 @@ with gr.Blocks() as demo:
     inp = gr.Textbox(label="اكتب سؤالك")
     out = gr.Textbox(label="الإجابة")
     btn = gr.Button("إرسال")
-    btn.click(fn=lambda x: rag_system(x), inputs=inp, outputs=out)
-
+    btn.click(rag_system, inp, out)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()"