BOT4U.py

import streamlit as st
from langchain_community.llms import HuggingFaceEndpoint
import os

# Set your Hugging Face API token
api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# Ensure API key is set properly
if not api_key:
    raise ValueError("Hugging Face API token not found. Set the environment variable 'HUGGINGFACEHUB_API_TOKEN'.")

repo_id = "mistralai/Mistral-7B-Instruct-v0.3"

# Create the language model endpoint for Mistral 7B
llm = HuggingFaceEndpoint(
    repo_id=repo_id,
    temperature=0.9,
    max_new_tokens=700
)

st.title('BOT4U')

# Sidebar menu for navigation
selected_section = st.sidebar.radio("Navigation", ["Text Generation", "Sentiment Analysis", "Text Summarizer"])

if selected_section == "Text Generation":
    st.header('Text Generation')

    prompt = st.text_input("Enter your prompt")
    
    if st.button("Generate"):
        if prompt:
            generated_text = llm(prompt)
            st.write(generated_text)
        else:
            st.write("Prompt not entered")

elif selected_section == "Sentiment Analysis":
    st.header('Sentiment Analysis')

    sentence = st.text_area("Enter a sentence for sentiment analysis")

    if st.button("Analyze"):
        if sentence:
            # Prepare a prompt for Mistral to analyze sentiment
            prompt = f"Analyze the sentiment of this sentence and classify it as Positive, Negative, or Neutral: '{sentence}'"
            response = llm(prompt)

            # Extract sentiment from the response
            if "Positive" in response:
                sentiment = "Positive"
            elif "Negative" in response:
                sentiment = "Negative"
            elif "Neutral" in response:
                sentiment = "Neutral"
            else:
                sentiment = "Unknown"

            # Display result
            st.write(f"The sentiment of the sentence '{sentence}' is: {sentiment}")
        else:
            st.write("Sentence not entered")

elif selected_section == "Text Summarizer":
    st.header('Text Summarizer')

    long_text = st.text_area("Enter text to summarize")

    if st.button("Summarize"):
        if long_text:
            # Use the Mistral model for summarization
            summarization_prompt = f"Summarize the following text: '{long_text}'"
            summary = llm(summarization_prompt)
            # Display the summary
            st.write(summary)
        else:
            st.write("Text not entered")