app.py

import os
import subprocess
import random
import streamlit as st
from groq import Groq
import language_tool_python
from language_tool_python.download_lt import download_lt

# Function to check and install Java
def install_java():
    try:
        st.write("Checking Java installation...")
        result = subprocess.run(["java", "-version"], check=True, capture_output=True, text=True)
        st.write("Java is already installed:", result.stderr.strip())
    except FileNotFoundError:
        st.write("Java is not installed. Installing now...")
        try:
            subprocess.run(["apt-get", "update"], check=True)
            subprocess.run(["apt-get", "install", "-y", "default-jdk"], check=True)
            st.write("Java installed successfully.")
        except Exception as e:
            st.error(f"Java installation failed: {str(e)}")
            raise e

# Function to ensure LanguageTool server files are downloaded
def ensure_languagetool_server():
    try:
        st.write("Downloading LanguageTool server files...")
        download_lt()
        st.write("LanguageTool server files downloaded successfully.")
    except Exception as e:
        st.error(f"Failed to download LanguageTool server files. Error: {str(e)}")
        raise e

# Function to initialize LanguageTool
def initialize_language_tool():
    try:
        st.write("Initializing LanguageTool...")
        ensure_languagetool_server()
        tool = language_tool_python.LanguageTool('en-US')
        st.write("LanguageTool initialized successfully.")
        return tool
    except Exception as e:
        st.error(f"Unable to initialize LanguageTool. Error: {e}")
        raise e

# Run Java installation check
install_java()

# Initialize Groq client
GROQ_API_KEY = "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"  # Replace with your Groq API key
os.environ["GROQ_API_KEY"] = GROQ_API_KEY
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

# Initialize LanguageTool
tool = None
try:
    tool = initialize_language_tool()
except Exception as e:
    st.error("Unable to initialize LanguageTool. Please ensure Java is installed, and all dependencies are configured.")
    st.error(f"Details: {str(e)}")

# Helper Functions
def adjust_sentence_structure(output_text):
    sentences = output_text.split(". ")
    adjusted_sentences = []
    for sentence in sentences:
        words = sentence.split()
        if len(words) > 20:  # Split overly long sentences
            split_point = random.randint(len(words) // 3, 2 * len(words) // 3)
            adjusted_sentences.append(" ".join(words[:split_point]))
            adjusted_sentences.append(" ".join(words[split_point:]))
        else:
            adjusted_sentences.append(sentence)
    return ". ".join(adjusted_sentences)

def check_grammar(output_text):
    if tool is None:
        return output_text
    matches = tool.check(output_text)
    return tool.correct(output_text)

def refine_humanization(output_text, tone, iteration):
    refinement_prompt = (
        f"Refine the following text to sound natural, human-like, and professional. "
        f"Preserve the original meaning, avoid irrelevant details, and align with a {tone} tone: {output_text}"
    )
    try:
        refinement_response = client.chat.completions.create(
            messages=[{"role": "user", "content": refinement_prompt}],
            model="llama-3.3-70b-versatile",
            stream=False,
        )
        refined_text = refinement_response.choices[0].message.content.strip()

        # Apply structural adjustments after the first iteration
        if iteration > 1:
            refined_text = adjust_sentence_structure(refined_text)

        return check_grammar(refined_text)
    except Exception:
        return output_text

def split_text_into_chunks(text, max_words=500):
    words = text.split()
    return [" ".join(words[i:i + max_words]) for i in range(0, len(words), max_words)]

# Streamlit App
st.title("Humanizer & Rephraser App (Groq-powered)")
st.subheader("Create human-like and grammatically accurate text")

# User Input
input_text = st.text_area("Enter text to process:", "")
input_word_count = len(input_text.split()) if input_text.strip() else 0
st.write(f"**Input Word Count:** {input_word_count}")

# Options
task_option = st.radio(
    "Choose an option:",
    ("Humanize Text", "Rephrase Text"),
    index=0
)
tone = st.selectbox("Select tone (for humanizing):", ["Casual", "Professional", "Neutral", "Engaging", "Friendly"])

# Depth of Humanization
humanization_depth = st.slider(
    "Select depth of humanization:",
    min_value=1,
    max_value=5,
    value=3,
    help="Higher values apply more refinements to make the text appear less AI-like."
)

# Generate Output
if st.button("Generate Output"):
    if not input_text.strip():
        st.error("Please enter some text to process.")
    else:
        try:
            with st.spinner("Processing..."):
                text_chunks = split_text_into_chunks(input_text, max_words=500)
                output_chunks = []

                for chunk in text_chunks:
                    task_prompt = (
                        f"{'Humanize' if task_option == 'Humanize Text' else 'Rephrase'} "
                        f"this text while ensuring clarity, professionalism, and original meaning: {chunk}"
                    )
                    chat_completion = client.chat.completions.create(
                        messages=[{"role": "user", "content": task_prompt}],
                        model="llama-3.3-70b-versatile",
                        stream=False,
                    )
                    output_text = chat_completion.choices[0].message.content.strip()
                    for i in range(humanization_depth):
                        output_text = refine_humanization(output_text, tone, i + 1)
                    output_chunks.append(output_text)

                final_output = " ".join(output_chunks)
                output_word_count = len(final_output.split())

                st.success("Done!")
                st.text_area("Generated Output:", value=final_output, height=300)
                st.write(f"**Output Word Count:** {output_word_count}")

        except Exception as e:
            st.error(f"An error occurred: {str(e)}")

# Footer
st.markdown("---")
st.markdown(
    "<p style='text-align: center; font-size: 14px;'>Designed by: <b>Engr. Makhdoom Muhammad Naeem Hashmi</b></p>",
    unsafe_allow_html=True
)