import streamlit as st
import re
from langdetect import detect
from transformers import pipeline
import nltk
from docx import Document
import io

# Download required NLTK resources
nltk.download('punkt')

# Updated tone categories
tone_categories = {
    "Emotional": ["urgent", "violence", "disappearances", "forced", "killing", "crisis"],
    "Critical": ["corrupt", "oppression", "failure", "repression", "unjust"],
    "Somber": ["tragedy", "loss", "pain", "sorrow", "mourning", "grief"],
    "Motivational": ["rise", "resist", "mobilize", "inspire", "courage", "change"],
    "Informative": ["announcement", "event", "scheduled", "update", "details"],
    "Positive": ["progress", "unity", "hope", "victory", "solidarity"],
    "Urgent": ["urgent", "violence", "disappearances", "forced", "killing", "concern", "crisis"],
    "Harsh": ["corrupt", "oppression", "failure", "repression", "exploit", "unjust"],
    "Negative": ["tragedy", "loss", "pain", "sorrow", "mourning", "grief"],
    "Empowering": ["rise", "resist", "mobilize", "inspire", "courage", "change"],
    "Neutral": ["announcement", "event", "scheduled", "update", "details", "protest on"],
    "Hopeful": ["progress", "unity", "hope", "victory", "together", "solidarity"]
}

# Updated frame categories
frame_categories = {
    "Human Rights & Justice": ["rights", "law", "justice", "legal", "humanitarian"],
    "Political & State Accountability": ["government", "policy", "state", "corruption", "accountability"],
    "Gender & Patriarchy": ["gender", "women", "violence", "patriarchy", "equality"],
    "Religious Freedom & Persecution": ["religion", "persecution", "minorities", "intolerance", "faith"],
    "Grassroots Mobilization": ["activism", "community", "movement", "local", "mobilization"],
    "Environmental Crisis & Activism": ["climate", "deforestation", "water", "pollution", "sustainability"],
    "Anti-Extremism & Anti-Violence": ["extremism", "violence", "hate speech", "radicalism", "mob attack"],
    "Social Inequality & Economic Disparities": ["class privilege", "labor rights", "economic", "discrimination"],
    "Activism & Advocacy": ["justice", "rights", "demand", "protest", "march", "campaign", "freedom of speech"],
    "Systemic Oppression": ["discrimination", "oppression", "minorities", "marginalized", "exclusion"],
    "Intersectionality": ["intersecting", "women", "minorities", "struggles", "multiple oppression"],
    "Call to Action": ["join us", "sign petition", "take action", "mobilize", "support movement"],
    "Empowerment & Resistance": ["empower", "resist", "challenge", "fight for", "stand up"],
    "Climate Justice": ["environment", "climate change", "sustainability", "biodiversity", "pollution"],
    "Human Rights Advocacy": ["human rights", "violations", "honor killing", "workplace discrimination", "law reform"]
}

# Detect language
def detect_language(text):
    try:
        return detect(text)
    except Exception as e:
        st.write(f"Error detecting language: {e}")
        return "unknown"

# Analyze tone based on predefined categories
def analyze_tone(text):
    detected_tones = set()
    for category, keywords in tone_categories.items():
        if any(word in text.lower() for word in keywords):
            detected_tones.add(category)

    if not detected_tones:
        tone_model = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
        model_result = tone_model(text, candidate_labels=list(tone_categories.keys()))
        detected_tones.update(model_result["labels"][:2])  

    return list(detected_tones)

# Extract hashtags
def extract_hashtags(text):
    return re.findall(r"#\w+", text)

# Extract frames based on predefined categories
def extract_frames(text):
    detected_frames = set()
    for category, keywords in frame_categories.items():
        if any(word in text.lower() for word in keywords):
            detected_frames.add(category)

    if not detected_frames:
        frame_model = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
        model_result = frame_model(text, candidate_labels=list(frame_categories.keys()))
        detected_frames.update(model_result["labels"][:2])  

    return list(detected_frames)

# Extract captions from DOCX file based on "Post X"
def extract_captions_from_docx(docx_file):
    doc = Document(docx_file)
    captions = {}
    current_post = None
    for para in doc.paragraphs:
        text = para.text.strip()
        if re.match(r"Post \d+", text, re.IGNORECASE):
            current_post = text
            captions[current_post] = []
        elif current_post:
            captions[current_post].append(text)

    return {post: " ".join(lines) for post, lines in captions.items() if lines}

# Generate a DOCX file in-memory with full captions
def generate_docx(output_data):
    doc = Document()
    doc.add_heading('Activism Message Analysis', 0)

    for index, (caption, result) in enumerate(output_data.items(), start=1):
        doc.add_heading(f"{index}. {caption}", level=1)
        doc.add_paragraph("Full Caption:")
        doc.add_paragraph(result['Full Caption'], style="Quote")

        doc.add_paragraph(f"Language: {result['Language']}")
        doc.add_paragraph(f"Tone of Caption: {', '.join(result['Tone of Caption'])}")
        doc.add_paragraph(f"Number of Hashtags: {result['Hashtag Count']}")
        doc.add_paragraph(f"Hashtags Found: {', '.join(result['Hashtags'])}")

        doc.add_heading('Frames:', level=2)
        for frame in result['Frames']:
            doc.add_paragraph(frame)

    doc_io = io.BytesIO()
    doc.save(doc_io)
    doc_io.seek(0)

    return doc_io

# Streamlit app
st.title('AI-Powered Activism Message Analyzer with Intersectionality')

st.write("Enter the text to analyze or upload a DOCX file containing captions:")

# Text Input
input_text = st.text_area("Input Text", height=200)

# File Upload
uploaded_file = st.file_uploader("Upload a DOCX file", type=["docx"])

# Initialize output dictionary
output_data = {}

if input_text:
    language = detect_language(input_text)
    tone = analyze_tone(input_text)
    hashtags = extract_hashtags(input_text)
    frames = extract_frames(input_text)

    output_data["Manual Input"] = {
        'Full Caption': input_text,
        'Language': language,
        'Tone of Caption': tone,
        'Hashtags': hashtags,
        'Hashtag Count': len(hashtags),
        'Frames': frames
    }

    st.success("Analysis completed for text input.")

if uploaded_file:
    captions = extract_captions_from_docx(uploaded_file)
    for caption, text in captions.items():
        language = detect_language(text)
        tone = analyze_tone(text)
        hashtags = extract_hashtags(text)
        frames = extract_frames(text)

        output_data[caption] = {
            'Full Caption': text,
            'Language': language,
            'Tone of Caption': tone,
            'Hashtags': hashtags,
            'Hashtag Count': len(hashtags),
            'Frames': frames
        }

    st.success(f"Analysis completed for {len(captions)} posts from the DOCX file.")

# Display results
if output_data:
    with st.expander("Generated Output"):
        st.subheader("Analysis Results")
        for index, (caption, result) in enumerate(output_data.items(), start=1):
            st.write(f"### {index}. {caption}")
            st.write("**Full Caption:**")
            st.write(f"> {result['Full Caption']}")
            st.write(f"**Language**: {result['Language']}")
            st.write(f"**Tone of Caption**: {', '.join(result['Tone of Caption'])}")
            st.write(f"**Number of Hashtags**: {result['Hashtag Count']}")
            st.write(f"**Hashtags Found:** {', '.join(result['Hashtags'])}")
            st.write("**Frames**:")
            for frame in result['Frames']:
                st.write(f"- {frame}")

    docx_file = generate_docx(output_data)

    if docx_file:
        st.download_button(
            label="Download Analysis as DOCX",
            data=docx_file,
            file_name="activism_message_analysis.docx",
            mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
        )