Create app.py

app.py

@@ -0,0 +1,83 @@
+import os
+import re
+import gradio as gr
+import qdrant_client
+from dotenv import load_dotenv
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Qdrant
+from langchain_openai import ChatOpenAI
+
+
+_ = load_dotenv()
+
+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)