Update app.py

app.py

@@ -6,75 +6,74 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import Qdrant
 from langchain_openai import ChatOpenAI
 
-
-QDRANT_URL= os.getenv('QDRANT_URL')
-QDRANT_API_KEY= os.getenv('QDRANT_API_KEY')
-OPEN_AI_TOKEN= os.getenv('OPEN_AI_TOKEN')
-
-
-def clean_text(text):
-    text = re.sub(r'<[^>]*>', '', text)
-    text = re.sub(r'[^\w\s]', '', text)
-    text = re.sub(r'\s+', ' ', text)
-    return text.lower().strip()
-
-
-collection_name = "Rag-with-Langchain-qdrant-Hadith"
-
-client = qdrant_client.QdrantClient(
-    url=QDRANT_URL,
-    api_key=QDRANT_API_KEY
-)
-
-collection_config = qdrant_client.http.models.VectorParams(
-    size = 384,
-    distance = qdrant_client.http.models.Distance.COSINE
-)
-
-embeddings = HuggingFaceEmbeddings(
-    model_name = "intfloat/multilingual-e5-small"
-)
-
-vectorStore = Qdrant(
-    client = client,
-    collection_name = collection_name,
-    embeddings = embeddings
-)
-
-
-def get_relevant_docs(question,k):
-  relevant_docs = vectorStore.similarity_search_with_score(query=question,k=k)
-  return relevant_docs
-
-def extract_contexts(relevant_docs):
-  contexts = []
-  for doc in relevant_docs:
-    contexts.append(doc[0].page_content)
-  return contexts
-
-def create_template(question,k):
-  relevant_docs = get_relevant_docs(question,k)
-  contexts = extract_contexts(relevant_docs)
-  template = f"""
-  Engage in a conversation with the user, responding to their question:
-    {question}
-    within this contexts of Hadiths:
-    {contexts} 
-    Encourage the model to provide informative and culturally sensitive answers, reflecting Islamic teachings. Maintain a conversational tone and aim for clarity in responses and make sure they are restricted extracted from the provided contexts and i want you to answer me in arabic."""
-  return template
-
-
-chat = ChatOpenAI(openai_api_key=OPEN_AI_TOKEN, model='gpt-3.5-turbo', temperature=0.5)
-
-def generate_answer(question):
-   cleaned_question= clean_text(question)
-   query = create_template(cleaned_question,10)
-   response = clean_text(chat.invoke(query).content)
-   return response
-
-def greet(question):
-    answer= generate_answer(question)
-    return answer
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch(inline = False)
+class HadithChatApp:
+    def __init__(self):
+        self.QDRANT_URL = os.getenv('QDRANT_URL')
+        self.QDRANT_API_KEY = os.getenv('QDRANT_API_KEY')
+        self.OPEN_AI_TOKEN = os.getenv('OPEN_AI_TOKEN')
+
+        self.collection_name = "Rag-with-Langchain-qdrant-Hadith"
+
+        self.client = qdrant_client.QdrantClient(
+            url=self.QDRANT_URL,
+            api_key=self.QDRANT_API_KEY
+        )
+
+        self.collection_config = qdrant_client.http.models.VectorParams(
+            size=384,
+            distance=qdrant_client.http.models.Distance.COSINE
+        )
+
+        self.embeddings = HuggingFaceEmbeddings(
+            model_name="intfloat/multilingual-e5-small"
+        )
+
+        self.vectorStore = Qdrant(
+            client=self.client,
+            collection_name=self.collection_name,
+            embeddings=self.embeddings
+        )
+
+        self.chat = ChatOpenAI(openai_api_key=self.OPEN_AI_TOKEN, model='gpt-3.5-turbo', temperature=0.5)
+
+    def clean_text(self, text):
+        text = re.sub(r'<[^>]*>', '', text)
+        text = re.sub(r'[^\w\s]', '', text)
+        text = re.sub(r'\s+', ' ', text)
+        return text.lower().strip()
+
+    def get_relevant_docs(self, question, k):
+        relevant_docs = self.vectorStore.similarity_search_with_score(query=question, k=k)
+        return relevant_docs
+
+    def extract_contexts(self, relevant_docs):
+        contexts = []
+        for doc in relevant_docs:
+            contexts.append(doc[0].page_content)
+        return contexts
+
+    def create_template(self, question, k):
+        relevant_docs = self.get_relevant_docs(question, k)
+        contexts = self.extract_contexts(relevant_docs)
+        template = f"""
+        Engage in a conversation with the user, responding to their question:
+        {question}
+        within this contexts of Hadiths:
+        {contexts} 
+        Encourage the model to provide informative and culturally sensitive answers, reflecting Islamic teachings. Maintain a conversational tone and aim for clarity in responses and make sure they are restricted extracted from the provided contexts and i want you to answer me in arabic."""
+        return template
+
+    def generate_answer(self, question):
+        cleaned_question = self.clean_text(question)
+        query = self.create_template(cleaned_question, 10)
+        response = self.clean_text(self.chat.invoke(query).content)
+        return response
+
+    def greet(self, question):
+        answer = self.generate_answer(question)
+        return answer
+
+if __name__ == "__main__":
+    hadith_chat_app = HadithChatApp()
+    iface = gr.Interface(fn=hadith_chat_app.greet, inputs="text", outputs="text")
+    iface.launch(inline=False)