Create app.py

app.py

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+import gradio as gr
+import requests
+import pandas as pd
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import CSVLoader
+from langchain_together import TogetherEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.vectorstores import Chroma
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+from langchain.document_loaders import CSVLoader
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain.vectorstores import Chroma
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain import PromptTemplate
+from langchain import LLMChain
+from langchain_together import Together
+import os
+
+
+os.environ['TOGETHER_API_KEY'] = "c2f52626b97118b71c0c36f66eda4f5957c8fc475e760c3d72f98ba07d3ed3b5"
+
+# Initialize global variable for vectorstore
+vectorstore = None
+embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
+llama3 = Together(model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", max_tokens=1024)
+def update_csv_files():
+    # Define the login URL and credentials
+    login_url = "https://livesystem.hisabkarlay.com/auth/login"
+    payload = {
+        "username": "user@123",
+        "password": "user@123",
+        "client_secret": "kNqJjlPkxyHdIKt3szCt4PYFWtFOdUheb8QVN8vQ",
+        "client_id": "5",
+        "grant_type": "password"
+    }
+
+    # Send a POST request to the login URL
+    response = requests.post(login_url, data=payload)
+
+    # Check the status and get the response data
+    if response.status_code == 200:
+        print("Login successful!")
+        access_token = response.json()['access_token']
+    else:
+        return f"Failed to log in: {response.status_code}"
+
+    # Profit loss Fetch report
+    report_url = "https://livesystem.hisabkarlay.com/connector/api/profit-loss-report"
+    headers = {
+        "Authorization": f"Bearer {access_token}"
+    }
+    response = requests.get(report_url, headers=headers)
+    profit_loss_data = response.json()['data']
+    keys = list(profit_loss_data.keys())
+    del keys[23]  # Adjust according to your needs
+    del keys[20]
+    del keys[19]
+    data_dict = {}
+    for key in keys:
+        data_dict[key] = profit_loss_data.get(key)
+    df = pd.DataFrame(data_dict, index=[0])
+    df.to_csv('profit_loss.csv', index=False)
+
+    # API call to get purchase-sell data
+    report_url = "https://livesystem.hisabkarlay.com/connector/api/purchase-sell"
+    response = requests.get(report_url, headers=headers)
+    sell_purchase_data = response.json()
+    sell_purchase_data = dict(list(sell_purchase_data.items())[2:])
+    df = pd.json_normalize(sell_purchase_data)
+    df.to_csv('purchase_sell_report.csv', index=False)
+
+    # API call to get trending product data
+    report_url = "https://livesystem.hisabkarlay.com/connector/api/trending-products"
+    response = requests.get(report_url, headers=headers)
+    trending_product_data = response.json()['data']
+    df = pd.DataFrame(trending_product_data)
+    df.columns = ['Product Units Sold', 'Product Name', 'Unit Type', 'SKU (Stock Keeping Unit)']
+    df.to_csv('trending_product.csv', index=False)
+
+    return "CSV files updated successfully!"
+
+def initialize_embedding():
+    global vectorstore
+    # Initialize the embedding function
+    
+
+    # Load CSV files
+    file_paths = [
+        "profit_loss.csv",
+        "purchase_sell_report.csv",
+        "trending_product.csv"
+    ]
+    documents = []
+    for path in file_paths:
+        loader = CSVLoader(path, encoding="windows-1252")
+        documents.extend(loader.load())  # Combine documents from all files
+
+    # Create an InMemoryVectorStore from the combined documents
+    vectorstore = InMemoryVectorStore.from_texts(
+        [doc.page_content for doc in documents],  # Extract the page_content from Document objects
+        embedding=embeddings,
+    )
+    return "Embeddings initialized successfully!"
+
+def qa_chain(query):
+    if vectorstore is None:
+        return "Please initialize the embeddings first."
+    
+    retriever = vectorstore.as_retriever()
+    retrieved_documents = retriever.invoke(query)
+    return retrieved_documents  # Not shown directly in the UI
+
+def generate_response(query):
+    if vectorstore is None:
+        return "Please initialize the embeddings first."
+    
+    retrieved_documents = qa_chain(query)  # Call qa_chain internally
+    chat_template = """
+    You are a highly intelligent and professional AI assistant.
+    Your role is to assist users by providing clear, concise, and accurate responses to their questions.
+
+    Context: {retrieved_documents}
+
+    Question: {query}
+
+    Please provide a professional, human-like answer that directly addresses the user's question. Ensure that the response is well-structured and easy to understand. Avoid using jargon that may be confusing.
+
+    Note: If the question involves historical places or historical heroes, do not provide a response.
+    """
+    prompt = PromptTemplate(
+        input_variables=['retrieved_documents', 'query'],
+        template=chat_template
+    )
+    
+    Generated_chat = LLMChain(llm=llama3, prompt=prompt)
+    response = Generated_chat.invoke({'retrieved_documents': retrieved_documents, 'query': query})
+    return response['text']
+
+def gradio_app():
+    with gr.Blocks() as app:
+        gr.Markdown("# Embedding and QA Interface")
+        
+        update_btn = gr.Button("Update CSV Files")
+        update_output = gr.Textbox(label="Update Output")
+
+        initialize_btn = gr.Button("Initialize Embedding")
+        initialize_output = gr.Textbox(label="Output")
+
+        query_input = gr.Textbox(label="Enter your query")
+        generate_response_btn = gr.Button("Generate Response")
+        response_output = gr.Textbox(label="Generated Response")
+        
+        # Button actions
+        update_btn.click(update_csv_files, outputs=update_output)
+        initialize_btn.click(initialize_embedding, outputs=initialize_output)
+        generate_response_btn.click(generate_response, inputs=query_input, outputs=response_output)
+
+    app.launch()
+
+# Run the Gradio app
+gradio_app()