Upload task.py

task.py

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+import os
+import streamlit as st
+from langchain_openai import ChatOpenAI
+from langchain.vectorstores import FAISS
+import pandas as pd
+
+# Environment setup
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+# Streamlit page setup
+st.set_page_config(
+    page_title="Airtel AI Agent System",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Sidebar setup
+with st.sidebar:
+    airtel_logo = "https://1000logos.net/wp-content/uploads/2023/06/Airtel-logo.jpg"  # Airtel logo URL
+    st.image(airtel_logo, use_column_width=True)
+    st.markdown("### Welcome to Airtel AI Help Bot")
+    st.markdown("**Instructions:**")
+    st.markdown("- Use the chat interface to ask questions about Airtel services.")
+    st.markdown("- You can also initiate specific tasks like SIM swap or plan details.")
+    st.markdown("Feel free to ask any queries you have!")
+
+# Apply custom CSS for the red-and-white theme
+st.markdown(
+    """
+    <style>
+    .stApp {
+        background-color: white;
+    }
+    header {
+        background-color: #e60000;
+    }
+    .stButton>button {
+        background-color: #e60000;
+        color: white;
+    }
+    .stTextInput>div>input {
+        background-color: #ffffff;
+        border: 2px solid #e60000;
+        color: black;
+    }
+    .css-1mgb8k9.e1fqkh3o3 {
+        background-color: white;
+    }
+    .css-18ni7ap.e8zbici0 {
+        background-color: white;
+    }
+    footer {
+        background-color: white;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Page title
+st.title("Airtel AI Agent System Demo")
+
+# Initialize session state to store chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# User input for chat interface
+user_input = st.text_input("Enter your message:")
+
+# Initialize agents' tasks
+def conversation_agent(state):
+    """Handles general conversations with the user."""
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
+    
+    try:
+        response = llm.invoke(state["question"])
+        # Ensure that response is not None
+        if response and response.content:
+            parsed_response = response.content  # Directly access content from AIMessage object
+        else:
+            parsed_response = "I'm sorry, I couldn't generate a response."
+    except Exception as e:
+        parsed_response = f"An error occurred: {e}"
+    
+    return {"question": state["question"], "response": parsed_response, "intent": "conversation"}
+
+def website_query_agent(state):
+    """Handles queries by retrieving relevant data from Airtel website stored in vector DB."""
+    vectorstore = FAISS.load_local("path_to_faiss_index")  # Assuming Airtel data is preloaded in FAISS
+    docs = vectorstore.similarity_search(state["question"])
+    response = "\n".join([doc.page_content for doc in docs[:3]])  # Return top 3 relevant docs
+    return {"question": state["question"], "response": response, "intent": "website_query"}
+
+def fallback_agent(state):
+    """Returns a fallback message for unrelated queries."""
+    return {"question": state["question"], "response": "Sorry, I don't have an answer for that.", "intent": "fallback"}
+
+def sim_swap_agent(state):
+    """Handles SIM swap workflow by triggering a form to collect user details."""
+    st.write("---Sim Swap Request---")
+    
+    # File path to store the data
+    file_path = "sim_swap_requests.xlsx"
+    
+    # Form for collecting user details
+    with st.form("sim_swap_form"):
+        name = st.text_input("Enter your name:")
+        phone_number = st.text_input("Enter your phone number:")
+        submit = st.form_submit_button("Submit")
+
+        if submit:
+            new_data = {"Name": [name], "Phone Number": [phone_number]}
+            df_new = pd.DataFrame(new_data)
+            
+            # Check if the Excel file already exists
+            if os.path.exists(file_path):
+                df_existing = pd.read_excel(file_path)
+                df_updated = pd.concat([df_existing, df_new], ignore_index=True)
+            else:
+                df_updated = df_new
+            
+            # Save the updated DataFrame to Excel
+            df_updated.to_excel(file_path, index=False)
+            return {"question": state["question"], "response": f"SIM Swap request submitted for {name} with phone number {phone_number}.", "intent": "sim_swap"}
+    
+    return {"question": state["question"], "response": "Please fill in your details for SIM swap.", "intent": "sim_swap"}
+
+def plan_details_agent(state):
+    """Handles plan details workflow by collecting phone number and returning a standard plan."""
+    with st.form("plan_details_form"):
+        phone_number = st.text_input("Enter your phone number:")
+        submit = st.form_submit_button("Submit")
+
+        if submit:
+            return {"question": state["question"], "response": f"Plan details for {phone_number}: You are on a standard plan of 999 INR/month.", "intent": "plan_details"}
+    
+    return {"question": state["question"], "response": "Please enter your phone number to check plan details.", "intent": "plan_details"}
+
+# Decision logic
+def decide_agent(state):
+    question = state["question"].lower()
+    if "sim swap" in question or "swap sim" in question:
+        return "sim_swap"
+    elif "plan details" in question or "active plan" in question:
+        return "plan_details"
+    elif "airtel" in question:
+        return "conversation"
+    elif "website" in question or "airtel website" in question:
+        return "website_query"
+    else:
+        return "fallback"
+
+# Workflow logic
+workflow = {
+    "conversation": conversation_agent,
+    "website_query": website_query_agent,
+    "sim_swap": sim_swap_agent,
+    "plan_details": plan_details_agent,
+    "fallback": fallback_agent,
+}
+
+# Function to handle user input and generate responses
+def handle_query(query):
+    state = {"question": query, "response": "", "intent": ""}
+    next_agent = decide_agent(state)
+    if next_agent in workflow:
+        agent = workflow[next_agent]
+        output = agent(state)
+        return output["response"]
+
+# Chat Interface
+if user_input:
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    bot_response = handle_query(user_input)
+    st.session_state.messages.append({"role": "bot", "content": bot_response})
+
+# Display chat messages in reverse order (latest message on top)
+for message in reversed(st.session_state.messages):
+    if message["role"] == "user":
+        st.chat_message("user").markdown(message["content"])
+    else:
+        st.chat_message("bot").markdown(message["content"])