app.py

import streamlit as st
import cv2
import numpy as np
from PIL import Image
import tempfile
import time

class DogLanguageInterpreter:
    def __init__(self):
        # Initialize any models or configurations here
        self.emotions = {
            'tail_wagging': 'Happy and excited',
            'ears_perked': 'Alert and interested',
            'lying_down': 'Relaxed or tired',
            'barking': 'Trying to communicate',
            'jumping': 'Enthusiastic and playful'
        }
    
    def analyze_video(self, video_file):
        # Placeholder for video analysis logic
        # In a real implementation, this would use CV models
        return {
            'primary_emotion': 'Happy and excited',
            'confidence': 0.85,
            'actions_detected': ['tail_wagging', 'jumping'],
            'recommendations': [
                'Your dog seems very playful!',
                'Consider engaging in some active play',
                'Reward this positive energy with treats'
            ]
        }

def main():
    st.set_page_config(
        page_title="Dog Language Interpreter",
        page_icon="🐕",
        layout="wide"
    )

    # Header
    st.title("🐕 Pet Dog Language Understanding")
    st.markdown("""
    Upload a video of your dog, and our AI will help interpret their behavior and emotions!
    """)

    # Initialize interpreter
    interpreter = DogLanguageInterpreter()

    # File uploader
    video_file = st.file_uploader("Upload a video of your dog", type=['mp4', 'mov', 'avi'])

    if video_file:
        # Save video to temporary file
        tfile = tempfile.NamedTemporaryFile(delete=False)
        tfile.write(video_file.read())

        # Display video
        st.video(tfile.name)

        # Analysis button
        if st.button("Analyze Dog Behavior"):
            with st.spinner("Analyzing your dog's behavior..."):
                # Simulate processing time
                time.sleep(2)
                
                # Get analysis results
                results = interpreter.analyze_video(tfile.name)

                # Display results in columns
                col1, col2 = st.columns(2)

                with col1:
                    st.subheader("Primary Emotion")
                    st.info(results['primary_emotion'])
                    st.metric("Confidence", f"{results['confidence']*100:.1f}%")

                with col2:
                    st.subheader("Actions Detected")
                    for action in results['actions_detected']:
                        st.success(action.replace('_', ' ').title())

                # Recommendations
                st.subheader("Recommendations")
                for rec in results['recommendations']:
                    st.write("• " + rec)

    # Additional information
    with st.expander("About Dog Body Language"):
        st.markdown("""
        ### Common Dog Behaviors and Their Meanings:
        - **Tail Wagging**: Usually indicates happiness, but the position matters
        - **Ears Position**: Can show alertness, submission, or aggression
        - **Body Posture**: Reveals confidence or anxiety levels
        - **Facial Expressions**: Can indicate stress, happiness, or attention
        
        Remember that each dog is unique and may express emotions differently!
        """)

if __name__ == "__main__":
    main()