Update app.py

app.py

@@ -1,5 +1,5 @@
-import pandas as pd
 import os
+import pandas as pd
 import gradio as gr
 from langchain.document_loaders import CSVLoader
 from langchain.vectorstores import FAISS
@@ -7,36 +7,34 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.chains import RetrievalQA
 from langchain_groq import ChatGroq
 
-# Set up your API key for ChatGroq
-os.environ["GROQ_API_KEY"] = "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"  # Replace with your actual API key
 
-# Initialize the HuggingFace embeddings
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")  # Lightweight embedding model
+api_key = os.environ.get("GROQ_API_KEY")
+if not api_key:
+    raise ValueError("Api key not found")
+
+os.environ["GROQ_API_KEY"] = api_key
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")  
+
 
-# Instantiate the ChatGroq model
 llm = ChatGroq(
-    model="mixtral-8x7b-32768",  # Replace with your desired model
+    model="mixtral-8x7b-32768", 
     temperature=0,
     max_tokens=None,
     timeout=None,
     max_retries=2
 )
 
-# Define the function to process the query and CSV
+#  function to process query and CSV
 def process_query(file, query):
     try:
-        # Load the CSV as documents for retrieval
         loader = CSVLoader(file_path=file.name)
         documents = loader.load()
 
-        # Create a FAISS vector store
+        # FAISS vector store
         vector_store = FAISS.from_documents(documents, embeddings)
-
-        # Create a retriever from the vector store
         retriever = vector_store.as_retriever()
-
-        # Create a RetrievalQA pipeline
-        qa_chain = RetrievalQA.from_chain_type(
+        
+        qa_chain = RetrievalQA.from_chain_type(  #RetrievalQA pipeline
             llm=llm,
             retriever=retriever,
             return_source_documents=True
@@ -52,20 +50,19 @@ def process_query(file, query):
     except Exception as e:
         return f"An error occurred: {str(e)}", ""
 
-# Create a Gradio interface
+# Gradio interface
 interface = gr.Interface(
     fn=process_query,
     inputs=[
-        gr.File(label="Upload CSV File"),  # File input for the CSV
-        gr.Textbox(label="Enter your query")  # Text input for the query
+        gr.File(label="Upload CSV File"), 
+        gr.Textbox(label="Enter your query") 
     ],
     outputs=[
-        gr.Textbox(label="Answer"),  # Text output for the answer
-        gr.Textbox(label="Source Documents")  # Text output for the source documents
+        gr.Textbox(label="Answer"), 
+        gr.Textbox(label="Source Documents")  # 
     ],
-    title="CSV Query Assistant",
-    description="Upload a CSV file and enter a query to retrieve relevant information."
+    title="DataScope.ai",
+    description="Upload & Unlock Insights from Your Data – Ask, Query, Discover!"
 )
 
-# Launch the Gradio app
 interface.launch(share=True)