Update app.py

app.py

@@ -1,50 +1,61 @@
-from langchain_community.vectorstores import Qdrant
-from langchain_huggingface import HuggingFaceEmbeddings
 import os
 from dotenv import load_dotenv
+from langchain_community.vectorstores import Qdrant
+from langchain_huggingface import HuggingFaceEmbeddings
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema.runnable import RunnablePassthrough
 from langchain.schema.output_parser import StrOutputParser
 from qdrant_client import QdrantClient, models
-from langchain_qdrant import Qdrant
-from langchain_qdrant import QdrantVectorStore
-from langchain_huggingface import ChatHuggingFace
-from langchain_openai import ChatOpenAI ,OpenAI
+from langchain_openai import ChatOpenAI
+import gradio as gr
+import logging
 
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # Load environment variables
 load_dotenv()
 
-HF_TOKEN = os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
+# HuggingFace API Token
+HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    logger.error("HF_TOKEN is not set in the environment variables.")
+    exit(1)
 
 # HuggingFace Embeddings
 embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
 
 # Qdrant Client Setup
-client = QdrantClient(
-    url=os.getenv("QDRANT_URL"),
-    api_key=os.getenv("QDRANT_API_KEY"),
-    prefer_grpc=True
-)
+try:
+    client = QdrantClient(
+        url=os.getenv("QDRANT_URL"),
+        api_key=os.getenv("QDRANT_API_KEY"),
+        prefer_grpc=True
+    )
+except Exception as e:
+    logger.error("Failed to connect to Qdrant. Ensure QDRANT_URL and QDRANT_API_KEY are correctly set.")
+    exit(1)
 
+# Define collection name
 collection_name = "mawared"
 
-# Try to create collection, handle if it already exists
+# Try to create collection
 try:
     client.create_collection(
         collection_name=collection_name,
         vectors_config=models.VectorParams(
             size=768,  # GTE-large embedding size
             distance=models.Distance.COSINE
-        ),
-        
+        )
     )
-    print(f"Created new collection: {collection_name}")
+    logger.info(f"Created new collection: {collection_name}")
 except Exception as e:
     if "already exists" in str(e):
-        print(f"Collection {collection_name} already exists, continuing...")
+        logger.info(f"Collection {collection_name} already exists, continuing...")
     else:
-        raise e
+        logger.error(f"Error creating collection: {e}")
+        exit(1)
 
 # Create Qdrant vector store
 db = Qdrant(
@@ -59,8 +70,13 @@ retriever = db.as_retriever(
     search_kwargs={"k": 5}
 )
 
-
-llm = ChatOpenAI(base_url="https://api-inference.huggingface.co/v1/", temperature=0 , api_key=HF_TOKEN , model="meta-llama/Llama-3.3-70B-Instruct")
+# Set up the LLM
+llm = ChatOpenAI(
+    base_url="https://api-inference.huggingface.co/v1/",
+    temperature=0,
+    api_key=HF_TOKEN,
+    model="meta-llama/Llama-3.3-70B-Instruct"
+)
 
 # Create prompt template
 template = """
@@ -91,7 +107,7 @@ Answer
 
 prompt = ChatPromptTemplate.from_template(template)
 
-# Create the RAG chain using LCEL with prompt printing and streaming output
+# Create the RAG chain
 rag_chain = (
     {"context": retriever, "question": RunnablePassthrough()}
     | prompt
@@ -99,19 +115,26 @@ rag_chain = (
     | StrOutputParser()
 )
 
+# Gradio Function
+def ask_question_gradio(question):
+    try:
+        response = ""
+        for chunk in rag_chain.stream(question):
+            response += chunk
+        return response
+    except Exception as e:
+        logger.error(f"Error during question processing: {e}")
+        return "An error occurred. Please try again later."
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=ask_question_gradio,
+    inputs=gr.Textbox(label="Ask a question about Mawared HR System:"),
+    outputs=gr.Textbox(label="Answer:"),
+    title="Mawared HR Assistant",
+    description="Ask questions about the Mawared HR system, and this assistant will provide answers based on the available context."
+)
 
-# Function to ask questions
-def ask_question(question):
-    print("Answer:\t", end=" ", flush=True)
-    for chunk in rag_chain.stream(question):
-        print(chunk, end="", flush=True)
-    print("\n")
-
-# Example usage
+# Launch the Gradio App
 if __name__ == "__main__":
-    while True:
-        user_question = input("\n \n \n Ask a question (or type 'quit' to exit): ")
-        if user_question.lower() == 'quit':
-            break
-        answer = ask_question(user_question)
-        # print("\nFull answer received.\n")
+    iface.launch()