Update app.py

app.py

@@ -1,91 +1,135 @@
+
 import streamlit as st
 from langchain_google_genai import ChatGoogleGenerativeAI
-import pandas as pd
-import io
+from transformers import pipeline
+import requests
+import streamlit_authenticator as stauth
+
+# Initialize the generative AI model
+llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"])
+
+# Load a pre-trained intent classification model
+classifier = pipeline("text-classification", model="distilbert-base-uncased")
+
+# Initialize session state
+if "conversation_history" not in st.session_state:
+    st.session_state.conversation_history = []
 
-st.title("Chat with Meow")
+if "feedback" not in st.session_state:
+    st.session_state.feedback = []
 
-# Initialize chat history if not already present
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
+# Define user authentication
+users = stauth.Authenticate(
+    names=['John Doe', 'Jane Smith'],
+    usernames=['johndoe', 'janesmith'],
+    passwords=['password1', 'password2'],
+    cookie_name='chat_auth',
+    key='abcdef'
+)
 
-def get_data_response(topic):
-    """Fetches data from the AI model based on the provided topic."""
-    prompt = f"Create a table of Data on {topic} for a data analysis project in a clean format."
+# Function to handle user queries and generate responses
+def generate_response(user_query):
+    prompt = f"User: {user_query}"
+    answers = llm.invoke(prompt)
+    return answers.content
+
+# Function to classify user intents using NLP
+def classify_intent(user_query):
+    result = classifier(user_query)[0]
+    label = result['label']
     
-    llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"])
-    try:
-        response = llm.invoke(prompt)
-        if response and hasattr(response, 'content'):
-            return response.content
-        else:
-            return "Sorry, I couldn't generate data for that topic."
-    except Exception as e:
-        return f"Error during data generation: {e}"
-
-def parse_response_to_df(response):
-    """Converts the response string to a DataFrame after cleaning up any formatting issues."""
-    try:
-        # Split response into lines
-        lines = response.split('\n')
-        
-        # Look for the start of the table and gather relevant lines
-        table_lines = []
-        is_table = False
-        
-        for line in lines:
-            if line.startswith("| Rank"):
-                is_table = True
-            if is_table and "|" in line:
-                # Clean line by removing unwanted characters and spaces
-                clean_line = line.strip().replace("∣", "|").replace(" ", "")
-                if clean_line:
-                    table_lines.append(clean_line)
-        
-        # Join lines and convert to DataFrame
-        cleaned_response = "\n".join(table_lines)
-        df = pd.read_csv(io.StringIO(cleaned_response), sep="|", skipinitialspace=True)
-        
-        # Strip whitespace from headers and cells
-        df.columns = df.columns.str.strip()
-        df = df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
-        
-        # Drop unnamed columns and rows where all elements are NaN
-        df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
-        df = df.dropna(how='all')
-        
-        return df
-    except Exception as e:
-        st.error(f"Error parsing response: {e}")
-        return None
-
-# Display the chat history
-for chat in st.session_state.chat_history:
-    st.info(f"You: {chat['user']}")
-    st.success(f"Meow: {chat['meow']}")
+    intents = {
+        "greeting": ["LABEL_0"],
+        "thanks": ["LABEL_1"],
+        "goodbye": ["LABEL_2"],
+        "help": ["LABEL_3"],
+        "custom_query": ["LABEL_4"]
+    }
     
-    df = parse_response_to_df(chat['meow'])
-    if df is not None:
-        st.table(df)
-
-# Create a form for new input
-with st.form("chat_form", clear_on_submit=True):
-    user_input = st.text_area("Enter Your Query:", key="input")
-    submitted = st.form_submit_button("Ask Meow")
-
-    if submitted and user_input:
-        # Get the response from the data generator
-        response = get_data_response(user_input)
-        
-        # Store the query and response in chat history
-        st.session_state.chat_history.append({"user": user_input, "meow": response})
+    for intent, labels in intents.items():
+        if label in labels:
+            return intent
+    return "unknown"
+
+# Function to update conversation history
+def update_conversation_history(user_query, bot_response):
+    st.session_state.conversation_history.append(f"You: {user_query}")
+    st.session_state.conversation_history.append(f"Bot: {bot_response}")
+
+# Function to display conversation history
+def display_conversation_history():
+    for message in st.session_state.conversation_history:
+        st.write(message)
+
+# Function to handle feedback
+def handle_feedback(feedback):
+    st.session_state.feedback.append(feedback)
+    st.write("Thank you for your feedback!")
+
+# Function to integrate with CRM system (pseudo-code)
+def integrate_with_crm(user_query, bot_response):
+    crm_endpoint = "https://crm.example.com/api/record_interaction"
+    data = {
+        "user_query": user_query,
+        "bot_response": bot_response
+    }
+    response = requests.post(crm_endpoint, json=data)
+    return response.status_code
+
+# Main Streamlit app
+def main():
+    st.sidebar.title("Customer Support Chatbot")
+    
+    # User authentication
+    name, authentication_status, username = users.login('Login', 'main')
+    
+    if authentication_status:
+        st.title(f"Hello, {name}!")
         
-        # Rerun to display the new chat entry
-        st.rerun()
+        display_conversation_history()
+
+        user_input = st.text_input("You:")
+        submit_button = st.button("Send")
+
+        if submit_button:
+            if user_input:
+                intent = classify_intent(user_input)
+                
+                if intent == "greeting":
+                    bot_response = "Hello! How can I help you today?"
+                elif intent == "thanks":
+                    bot_response = "You're welcome!"
+                elif intent == "goodbye":
+                    bot_response = "Goodbye! Have a great day."
+                elif intent == "help":
+                    bot_response = "I can assist you with account issues, billing questions, product support, and technical issues. Please specify your query."
+                elif intent == "custom_query":
+                    bot_response = generate_response(user_input)
+                else:
+                    bot_response = "I'm sorry, I didn't understand that. How can I assist you?"
+
+                update_conversation_history(user_input, bot_response)
+                display_conversation_history()
+                
+                # Integrate with CRM
+                integrate_with_crm(user_input, bot_response)
+                
+                user_input = ""
+    
+        feedback = st.text_input("Provide Feedback:")
+        feedback_button = st.button("Submit Feedback")
+
+        if feedback_button:
+            if feedback:
+                handle_feedback(feedback)
+                st.text_input("Provide Feedback:", value="", key="feedback_clear")
+
+    elif authentication_status == False:
+        st.error('Username/password is incorrect')
+    elif authentication_status == None:
+        st.warning('Please enter your username and password')
 
-# Optionally, allow clearing chat history
-if st.button("Clear Chat History"):
-    st.session_state.chat_history = []
-    st.rerun()
+if __name__ == "__main__":
+    main()