import gradio as gr
from textblob import TextBlob
from deepface import DeepFace
import cv2
import moviepy.editor as mp
import tempfile
import os

# Analyze text
def analyze_text(text):
    blob = TextBlob(text)
    polarity = blob.sentiment.polarity
    emotion = "Positive" if polarity > 0 else "Negative" if polarity < 0 else "Neutral"
    return f"Sentiment: {emotion} (Score: {polarity:.2f})"

# Analyze face
def analyze_face(image):
    try:
        result = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False)
        dominant_emotion = result[0]['dominant_emotion']
        return f"Dominant Emotion: {dominant_emotion}"
    except Exception as e:
        return f"Error analyzing face: {str(e)}"

# Analyze video
def analyze_video(video_path):
    try:
        temp_folder = tempfile.mkdtemp()
        clip = mp.VideoFileClip(video_path)
        frame = clip.get_frame(clip.duration / 2)
        frame_path = os.path.join(temp_folder, "frame.jpg")
        cv2.imwrite(frame_path, cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
        result = DeepFace.analyze(frame_path, actions=['emotion'], enforce_detection=False)
        dominant_emotion = result[0]['dominant_emotion']
        return f"Dominant Emotion in Video: {dominant_emotion}"
    except Exception as e:
        return f"Error analyzing video: {str(e)}"

# Build Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 Emotion Decoder - Sentiment & Emotion Analysis")
    
    with gr.Tab("Text Analysis"):
        text_input = gr.Textbox(label="Enter text")
        text_output = gr.Textbox(label="Sentiment Result")
        text_button = gr.Button("Analyze Text")
        text_button.click(analyze_text, inputs=text_input, outputs=text_output)
    
    with gr.Tab("Face Emotion Detection"):
        img_input = gr.Image(type="filepath", label="Upload an Image")
        img_output = gr.Textbox(label="Emotion Result")
        img_button = gr.Button("Analyze Face Emotion")
        img_button.click(analyze_face, inputs=img_input, outputs=img_output)
    
    with gr.Tab("Video Emotion Detection"):
        video_input = gr.Video(label="Upload a Video")
        video_output = gr.Textbox(label="Emotion Result")
        video_button = gr.Button("Analyze Video Emotion")
        video_button.click(analyze_video, inputs=video_input, outputs=video_output)

demo.launch()