import gradio as gr
import re
import logging
from langdetect import detect
from transformers import pipeline
from docx import Document
import io
from dotenv import load_dotenv
from groq import ChatGroq

# Load environment variables
load_dotenv()

# Initialize logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

# Initialize LLM
llm = ChatGroq(temperature=0.5, groq_api_key="GROQ_API_KEY", model_name="llama3-8b-8192")

# Updated tone categories
tone_categories = {
    "Emotional": ["urgent", "violence", "disappearances", "forced", "killing", "crisis", "concern"],
    "Harsh": ["corrupt", "oppression", "failure", "repression", "exploit", "unjust", "authoritarian"],
    "Somber": ["tragedy", "loss", "pain", "sorrow", "mourning", "grief", "devastation"],
    "Motivational": ["rise", "resist", "mobilize", "inspire", "courage", "change", "determination"],
    "Informative": ["announcement", "event", "scheduled", "update", "details", "protest", "statement"],
    "Positive": ["progress", "unity", "hope", "victory", "together", "solidarity", "uplifting"],
    "Happy": ["joy", "celebration", "cheer", "success", "smile", "gratitude", "harmony"],
    "Angry": ["rage", "injustice", "fury", "resentment", "outrage", "betrayal"],
    "Fearful": ["threat", "danger", "terror", "panic", "risk", "warning"],
    "Sarcastic": ["brilliant", "great job", "amazing", "what a surprise", "well done", "as expected"],
    "Hopeful": ["optimism", "better future", "faith", "confidence", "looking forward"]
}

# 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:
        logging.error(f"Error detecting language: {e}")
        return "unknown"

# Analyze tone based on predefined categories
def analyze_tone(text):
    response = llm.chat(text, system_prompt="Identify the primary tones in this text based on the given tone categories.")
    return response

# Extract frames based on predefined categories
def extract_frames(text):
    response = llm.chat(text, system_prompt="Identify the primary frames in this text based on the given frame categories.")
    return response

# Categorize frame importance
def categorize_frame_importance(text):
    response = llm.chat(text, system_prompt="Categorize the identified frames as Major Frame, Significant Frame, or Minor Mention based on their relevance to the text.")
    return response

# Gradio Interface
def analyze_text(text):
    language = detect_language(text)
    tone = analyze_tone(text)
    frames = extract_frames(text)
    frame_importance = categorize_frame_importance(text)
    return f"Language: {language}\nTones: {tone}\nFrames: {frames}\nFrame Importance: {frame_importance}"

demo = gr.Interface(
    fn=analyze_text,
    inputs="text",
    outputs="text",
    title="AI-Powered Activism Message Analyzer with Intersectionality"
)

demo.launch()