a.py

import streamlit as st
import pandas as pd
from fuzzywuzzy import process
from langchain_community.llms import LlamaCpp
from langchain_core.callbacks import StreamingStdOutCallbackHandler
from langchain_core.prompts import PromptTemplate

# Load the CSV files into DataFrames with Windows-1252 encoding
df = pd.read_csv('location.csv', encoding='Windows-1252')
df2 = pd.read_csv('train.csv')

# Initialize the LlamaCpp model
llm = LlamaCpp(
    model_path="unsloth.Q5_K_M.gguf",
    temperature=0.01,
    max_tokens=500,
    top_p=3,
    callbacks=[StreamingStdOutCallbackHandler()],
    verbose=False,
    stop=["###"]
)

# Define the prompt template
template = """Below is an instruction that describes a task, paired with an input that provides further context. Write a lengthy detailed response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:
{response}"""

prompt = PromptTemplate.from_template(template)


# Function to find the best matching context based on user input
def find_best_match(query):
    questions = df2['Question'].tolist()
    contexts = df2['Context'].tolist()

    # Find the best match
    best_match = process.extractOne(query, questions)
    if best_match:
        index = questions.index(best_match[0])
        return contexts[index]
    return "No relevant information found."


# Function to truncate response at the nearest full stop
def truncate_at_full_stop(text, max_length=500):
    if len(text) <= max_length:
        return text

    truncated = text[:max_length]
    print(f"Truncated text: {truncated}")

    last_period = truncated.rfind('.')
    print(f"Last period index: {last_period}")

    if last_period != -1:
        return truncated[:last_period + 1]

    return truncated


# Initialize session state for selected service, chat history, and AI history
if 'selected_service' not in st.session_state:
    st.session_state.selected_service = "Home"
if 'chat_history' not in st.session_state:
    st.session_state.chat_history = []
if 'history' not in st.session_state:
    st.session_state.history = []
if 'input' not in st.session_state:
    st.session_state['input'] = ''

# Sidebar for selecting services
with st.sidebar:
    st.title("Select the Service")

    # Create buttons for each service
    if st.button('Medicine Services'):
        st.session_state.selected_service = "Medicine Services"

    if st.button('Kendra Locator'):
        st.session_state.selected_service = "Kendra Locator"

    if st.button('Assistant'):
        st.session_state.selected_service = "Assistant"

# Main content area based on selected service
if st.session_state.selected_service == "Home":
    st.title("Welcome to Medical Service Center")
    st.write("Explore the options in the sidebar to get started.")

elif st.session_state.selected_service == "Medicine Services":
    st.title("Medicine Services")

    # Display chat history
    for chat in st.session_state.chat_history:
        st.write(f"**User:** {chat['user']}")
        st.write(f"**Bot:** {chat['bot']}")

    # User input section
    def handle_input():
        user_input = st.session_state['input']
        if user_input:
            response = find_best_match(user_input)
            st.session_state.chat_history.append({"user": user_input, "bot": response})
            st.session_state['input'] = ''

    # Persistent text input at the top
    st.text_input("Enter medicine:", key="input", on_change=handle_input)

elif st.session_state.selected_service == "Kendra Locator":
    st.title("Kendra Locator")
    display_option = st.selectbox("Select:", ["Address", "Email"])
    pin_code_input = st.text_input("Enter Pin Code:")

    if st.button("Locate"):
        if pin_code_input:
            result = df[df['Pin'].astype(str) == pin_code_input]
            if not result.empty:
                if display_option == "Address":
                    st.write(f"Address: {result['Address'].values[0]}")
                elif display_option == "Email":
                    st.write(f"Email: {result['Email'].values[0]}")
            else:
                st.write("No results found.")
        else:
            st.write("Please enter a pin code.")

elif st.session_state.selected_service == "Assistant":
    st.title("Query Assistance")

    # Display AI chat history
    for chat in st.session_state.history:
        st.write(f"**User Query:** {chat['user']}")
        st.write(f"**Chatbot:** {chat['bot']}")

    # Function to handle user input
    def handle_input():
        user_input = st.session_state['input']
        if user_input:
            # Format the prompt
            formatted_prompt = prompt.format(
                instruction="You are an all-knowing Medical AI. Provide detailed responses to only medicine-related queries.",
                input=user_input,
                response=""  # Leave this blank for generation!
            )

            # Generate response
            response = llm.invoke(formatted_prompt)

            # Truncate response if necessary
            truncated_response = truncate_at_full_stop(response)

            # Update the chat history
            st.session_state.history.append({"user": user_input, "bot": truncated_response})

            # Clear the input box
            st.session_state['input'] = ''

    # Persistent text input at the top
    st.text_input("Enter Query:", key="input", on_change=handle_input)