app/routes.py

from flask import Blueprint, request, jsonify
from werkzeug.utils import secure_filename
import os
import easyocr
import pytesseract  # Ensure this is imported
from PIL import Image
from app.models import audio_model, sentiment_pipeline, emotion_pipeline
from app.services import extract_tasks
from app.utils import generate_tags, error_response

# Initialize Flask Blueprint
bp = Blueprint('main', __name__)

# Initialize the EasyOCR reader for English only (disable GPU if not available)
reader = easyocr.Reader(['en'], gpu=False)

EMOTION_SCORE_THRESHOLD = 0.15  # Adjust based on your testing
MIN_SENTIMENT_CONFIDENCE = 0.4  # Below this becomes "neutral"

# =============================
# 🔹 API Routes
# =============================

@bp.route('/transcribe', methods=['POST'])
def transcribe():
    if 'file' not in request.files:
        return error_response("No file provided", 400)

    file = request.files['file']
    file_path = os.path.join("/tmp", secure_filename(file.filename))
    file.save(file_path)

    try:
        # Transcribe Audio
        result = audio_model.transcribe(file_path)
        transcription = result.get("text", "")

        if not transcription.strip():
            return error_response("Transcription is empty", 400)

        # Send transcription to /analyze_text API
        analysis_response = analyze_text_internal(transcription)
        tags = generate_tags(transcription)  # Function to extract tags from text

        return jsonify({
            "transcription": transcription,
            "sentiment": analysis_response["sentiment"],
            "emotion": analysis_response["emotion"],
            "confidence": analysis_response["confidence"],
            "tags": tags
        })
    except Exception as e:
        return error_response(str(e), 500)


@bp.route('/analyze_image', methods=['POST'])
def analyze_image():
    if 'file' not in request.files:
        return error_response("No image file provided", 400)

    file = request.files['file']
    filename = secure_filename(file.filename)
    file_path = os.path.join("/tmp", filename)
    file.save(file_path)

    try:
        # Use EasyOCR in detail mode to get confidence scores
        results = reader.readtext(file_path, detail=1)

        # Filter out entries with low confidence (e.g., below 0.5)
        filtered_texts = [text for bbox, text, conf in results if conf > 0.5]
        extracted_text = "\n".join(filtered_texts)

        print("Filtered Extracted text:", extracted_text)

        if not extracted_text.strip():
            return error_response("No meaningful text found in image", 400)

        # Analyze the extracted text to get sentiment, emotion, etc.
        analysis_response = analyze_text_internal(extracted_text)
        tags = generate_tags(extracted_text)

        return jsonify({
            "extracted_text": extracted_text.strip(),
            "sentiment": analysis_response.get("sentiment"),
            "emotion": analysis_response.get("emotion"),
            "confidence": analysis_response.get("confidence"),
            "tags": tags
        })
    except Exception as e:
        return error_response(str(e), 500)


# Internal function to call analyze_text directly
def analyze_text_internal(text):
    try:
        # Get sentiment (positive/neutral/negative)
        sentiment = sentiment_pipeline(text)[0]

        # Get dominant emotion (anger/disgust/fear/joy/neutral/sadness/surprise)
        emotion = emotion_pipeline(text)[0][0]

        return {
            "sentiment": sentiment['label'],
            "emotion": emotion['label'],
            "confidence": {
                "sentiment": round(sentiment['score'], 3),
                "emotion": round(emotion['score'], 3)
            }
        }
    except Exception as e:
        print(f"Analysis error: {str(e)}")
        return error_response(f"Processing error: {str(e)}", 500)


@bp.route('/analyze_text', methods=['POST'])
def analyze_text():
    data = request.json
    if not data or 'text' not in data:
        return error_response("No text provided", 400)

    text = data['text'].strip().lower()

    try:
        # Get sentiment (positive/neutral/negative)
        sentiment = sentiment_pipeline(text)[0]

        # Get dominant emotion (anger/disgust/fear/joy/neutral/sadness/surprise)
        emotion = emotion_pipeline(text)[0][0]

        tags = generate_tags(text)

        return {
            "sentiment": sentiment['label'],
            "emotion": emotion['label'],
            "confidence": {
                "sentiment": round(sentiment['score'], 3),
                "emotion": round(emotion['score'], 3)
            },
            "tags": tags
        }
    except Exception as e:
        print(f"Analysis error: {str(e)}")
        return error_response(f"Processing error: {str(e)}", 500)


# 📌 3. Extract Actionable Tasks
@bp.route('/extract_actions', methods=['POST'])
def extract_actions():
    data = request.json
    if not data or 'text' not in data:
        return error_response("No text provided", 400)

    text = data['text']
    try:
        tasks = extract_tasks(text)
        return jsonify({"tasks": tasks})
    except Exception as e:
        return error_response(str(e), 500)


# =============================
# 🔹 Error Handling
# =============================

@bp.errorhandler(404)
def not_found_error(error):
    return error_response("Not Found", 404)

@bp.errorhandler(500)
def internal_error(error):
    return error_response("Internal Server Error", 500)