import streamlit as st
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import torch
import json
import os
import glob
from pathlib import Path
from datetime import datetime
import edge_tts
import asyncio
import base64
import requests
import plotly.graph_objects as go
from gradio_client import Client
from collections import defaultdict
from bs4 import BeautifulSoup
from audio_recorder_streamlit import audio_recorder
import streamlit.components.v1 as components

# Page configuration
st.set_page_config(
    page_title="Video Search & Research Assistant",
    page_icon="🎥",
    layout="wide",
    initial_sidebar_state="auto",
)

# Initialize session state
if 'search_history' not in st.session_state:
    st.session_state['search_history'] = []
if 'last_voice_input' not in st.session_state:
    st.session_state['last_voice_input'] = ""
if 'transcript_history' not in st.session_state:
    st.session_state['transcript_history'] = []
if 'should_rerun' not in st.session_state:
    st.session_state['should_rerun'] = False

# Custom styling
st.markdown("""
<style>
    .main { background: linear-gradient(to right, #1a1a1a, #2d2d2d); color: #fff; }
    .stMarkdown { font-family: 'Helvetica Neue', sans-serif; }
    .stButton>button { margin-right: 0.5rem; }
</style>
""", unsafe_allow_html=True)

# Initialize components
speech_component = components.declare_component("speech_recognition", path="mycomponent")

class VideoSearch:
    def __init__(self):
        self.text_model = SentenceTransformer('all-MiniLM-L6-v2')
        self.load_dataset()
        
    def fetch_dataset_rows(self):
        """Fetch dataset from Hugging Face API with debug and caching"""
        try:
            st.info("Fetching from Hugging Face API...")
            url = "https://datasets-server.huggingface.co/first-rows?dataset=omegalabsinc%2Fomega-multimodal&config=default&split=train"
            
            response = requests.get(url, timeout=30)
            st.write(f"Response status: {response.status_code}")
            
            if response.status_code == 200:
                data = response.json()
                
                if 'rows' in data:
                    # Extract actual row data from the nested structure
                    processed_rows = []
                    for row_data in data['rows']:
                        if 'row' in row_data:  # Access the nested 'row' data
                            processed_rows.append(row_data['row'])
                    
                    df = pd.DataFrame(processed_rows)
                    
                    # Debug output
                    st.write("DataFrame columns after processing:", list(df.columns))
                    st.write("Number of rows:", len(df))
                    
                    return df
                else:
                    st.error("No 'rows' found in API response")
                    st.write("Raw API Response:", data)
                    return self.load_example_data()
            else:
                st.error(f"API request failed with status code: {response.status_code}")
                return self.load_example_data()
                
        except Exception as e:
            st.error(f"Error fetching dataset: {str(e)}")
            return self.load_example_data()

    def load_example_data(self):
        """Load example data as fallback"""
        example_data = [
            {
                "video_id": "cd21da96-fcca-4c94-a60f-0b1e4e1e29fc",
                "youtube_id": "IO-vwtyicn4",
                "description": "This video shows a close-up of an ancient text carved into a surface, with the text appearing to be in a cursive script.",
                "views": 45489,
                "start_time": 1452,
                "end_time": 1458,
                "video_embed": [0.014160037972033024, -0.003111184574663639, -0.016604168340563774],
                "description_embed": [-0.05835828185081482, 0.02589797042310238, 0.11952091753482819]
            },
            {
                "video_id": "a8ebde7d-d717-4c1e-8be4-bdb4bc0c544f",
                "youtube_id": "mo4rEyF7gTE",
                "description": "This video shows a close-up view of a classical architectural structure, featuring stone statues with ornate details.",
                "views": 4468,
                "start_time": 318,
                "end_time": 324,
                "video_embed": [0.015160037972033024, -0.004111184574663639, -0.017604168340563774],
                "description_embed": [-0.06835828185081482, 0.03589797042310238, 0.12952091753482819]
            },
            {
                "video_id": "d1be64a6-22e2-4fbd-a176-20749e7c3d8a",
                "youtube_id": "IO-vwtyicn4",
                "description": "This video shows a weathered ancient painting depicting figures in classical style with vibrant colors preserved.",
                "views": 45489,
                "start_time": 1698,
                "end_time": 1704,
                "video_embed": [0.016160037972033024, -0.005111184574663639, -0.018604168340563774],
                "description_embed": [-0.07835828185081482, 0.04589797042310238, 0.13952091753482819]
            }
        ]
        return pd.DataFrame(example_data)
    
    def prepare_features(self):
        """Prepare and cache embeddings"""
        try:
            if 'video_embed' not in self.dataset.columns:
                st.warning("Using example data embeddings")
                self.dataset = self.load_example_data()
            
            # Debug: Show raw data types and first row
            st.write("Data Types:", self.dataset.dtypes)
            st.write("\nFirst row of embeddings:")
            st.write("video_embed type:", type(self.dataset['video_embed'].iloc[0]))
            st.write("video_embed content:", self.dataset['video_embed'].iloc[0])
            st.write("\ndescription_embed type:", type(self.dataset['description_embed'].iloc[0]))
            st.write("description_embed content:", self.dataset['description_embed'].iloc[0])

            # Convert string representations of embeddings back to numpy arrays
            def safe_eval_list(s):
                try:
                    # Clean the string representation
                    if isinstance(s, str):
                        s = s.replace('[', '').replace(']', '').strip()
                        # Split by whitespace and/or commas
                        numbers = [float(x.strip()) for x in s.split() if x.strip()]
                        return numbers
                    elif isinstance(s, list):
                        return [float(x) for x in s]
                    else:
                        st.error(f"Unexpected type for embedding: {type(s)}")
                        return None
                except Exception as e:
                    st.error(f"Error parsing embedding: {str(e)}")
                    st.write("Problematic string:", s)
                    return None

            # Process embeddings with detailed error reporting
            video_embeds = []
            text_embeds = []
            
            for idx in range(len(self.dataset)):
                try:
                    video_embed = safe_eval_list(self.dataset['video_embed'].iloc[idx])
                    desc_embed = safe_eval_list(self.dataset['description_embed'].iloc[idx])
                    
                    if video_embed is not None and desc_embed is not None:
                        video_embeds.append(video_embed)
                        text_embeds.append(desc_embed)
                    else:
                        st.warning(f"Skipping row {idx} due to parsing failure")
                except Exception as e:
                    st.error(f"Error processing row {idx}: {str(e)}")
                    st.write("Row data:", self.dataset.iloc[idx])

            if video_embeds and text_embeds:
                try:
                    self.video_embeds = np.array(video_embeds)
                    self.text_embeds = np.array(text_embeds)
                    st.success(f"Successfully processed {len(video_embeds)} embeddings")
                    st.write("Video embeddings shape:", self.video_embeds.shape)
                    st.write("Text embeddings shape:", self.text_embeds.shape)
                except Exception as e:
                    st.error(f"Error converting to numpy arrays: {str(e)}")
            else:
                st.warning("No valid embeddings found, using random embeddings")
                num_rows = len(self.dataset)
                self.video_embeds = np.random.randn(num_rows, 384)
                self.text_embeds = np.random.randn(num_rows, 384)
            
        except Exception as e:
            st.error(f"Error preparing features: {str(e)}")
            import traceback
            st.write("Traceback:", traceback.format_exc())
            # Create random embeddings as fallback
            num_rows = len(self.dataset)
            self.video_embeds = np.random.randn(num_rows, 384)
            self.text_embeds = np.random.randn(num_rows, 384)

    def load_dataset(self):
        try:
            self.dataset = self.fetch_dataset_rows()
            if self.dataset is not None:
                self.prepare_features()
            else:
                self.create_dummy_data()
        except Exception as e:
            st.error(f"Error loading dataset: {e}")
            self.create_dummy_data()
    
    def prepare_features(self):
        try:
            self.video_embeds = np.array([json.loads(e) if isinstance(e, str) else e 
                                        for e in self.dataset.video_embed])
            self.text_embeds = np.array([json.loads(e) if isinstance(e, str) else e 
                                       for e in self.dataset.description_embed])
        except Exception as e:
            st.error(f"Error preparing features: {e}")
            num_rows = len(self.dataset)
            self.video_embeds = np.random.randn(num_rows, 384)
            self.text_embeds = np.random.randn(num_rows, 384)
    
    def search(self, query, top_k=5):
        query_embedding = self.text_model.encode([query])[0]
        
        video_sims = cosine_similarity([query_embedding], self.video_embeds)[0]
        text_sims = cosine_similarity([query_embedding], self.text_embeds)[0]
        
        combined_sims = 0.5 * video_sims + 0.5 * text_sims
        top_indices = np.argsort(combined_sims)[-top_k:][::-1]
        
        results = []
        for idx in top_indices:
            results.append({
                'video_id': self.dataset.iloc[idx]['video_id'],
                'youtube_id': self.dataset.iloc[idx]['youtube_id'],
                'description': self.dataset.iloc[idx]['description'],
                'start_time': self.dataset.iloc[idx]['start_time'],
                'end_time': self.dataset.iloc[idx]['end_time'],
                'relevance_score': float(combined_sims[idx]),
                'views': self.dataset.iloc[idx]['views']
            })
        return results

def perform_arxiv_search(query, vocal_summary=True, extended_refs=False):
    """Perform Arxiv search with audio summaries"""
    try:
        client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
        refs = client.predict(query, 20, "Semantic Search", 
                            "mistralai/Mixtral-8x7B-Instruct-v0.1",
                            api_name="/update_with_rag_md")[0]
        response = client.predict(query, "mistralai/Mixtral-8x7B-Instruct-v0.1",
                                True, api_name="/ask_llm")
        
        result = f"### 🔎 {query}\n\n{response}\n\n{refs}"
        st.markdown(result)
        
        if vocal_summary:
            audio_file = asyncio.run(generate_speech(response[:500]))
            if audio_file:
                st.audio(audio_file)
                os.remove(audio_file)
        
        return result
    except Exception as e:
        st.error(f"Error in Arxiv search: {e}")
        return None

async def generate_speech(text, voice="en-US-AriaNeural"):
    """Generate speech using Edge TTS"""
    if not text.strip():
        return None
    
    try:
        communicate = edge_tts.Communicate(text, voice)
        audio_file = f"speech_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp3"
        await communicate.save(audio_file)
        return audio_file
    except Exception as e:
        st.error(f"Error generating speech: {e}")
        return None

def process_audio_input(audio_bytes):
    """Process audio input from recorder"""
    if audio_bytes:
        # Save temporary file
        audio_path = f"temp_audio_{datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
        with open(audio_path, "wb") as f:
            f.write(audio_bytes)
        
        # Here you would typically use a speech-to-text service
        # For now, we'll just acknowledge the recording
        st.success("Audio recorded successfully!")
        
        # Cleanup
        if os.path.exists(audio_path):
            os.remove(audio_path)
        
        return True
    return False

def main():
    st.title("🎥 Video Search & Research Assistant")
    
    # Initialize search
    search = VideoSearch()
    
    # Create main tabs
    tab1, tab2, tab3 = st.tabs(["🔍 Video Search", "🎙️ Voice & Audio", "📚 Arxiv Research"])
    
    with tab1:
        st.subheader("Search Video Dataset")
        
        # Text search
        query = st.text_input("Enter your search query:")
        col1, col2 = st.columns(2)
        
        with col1:
            search_button = st.button("🔍 Search")
        with col2:
            num_results = st.slider("Number of results:", 1, 10, 5)
            
        if search_button and query:
            results = search.search(query, num_results)
            st.session_state['search_history'].append({
                'query': query,
                'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                'results': results
            })
            
            for i, result in enumerate(results, 1):
                with st.expander(f"Result {i}: {result['description'][:100]}...", expanded=i==1):
                    cols = st.columns([2, 1])
                    
                    with cols[0]:
                        st.markdown(f"**Full Description:**")
                        st.write(result['description'])
                        st.markdown(f"**Time Range:** {result['start_time']}s - {result['end_time']}s")
                        st.markdown(f"**Views:** {result['views']:,}")
                    
                    with cols[1]:
                        st.markdown(f"**Relevance Score:** {result['relevance_score']:.2%}")
                        if result['youtube_id']:
                            st.video(f"https://youtube.com/watch?v={result['youtube_id']}&t={result['start_time']}")
                        
                        # Generate audio summary
                        if st.button(f"🔊 Generate Audio Summary", key=f"audio_{i}"):
                            summary = f"Video summary: {result['description'][:200]}"
                            audio_file = asyncio.run(generate_speech(summary))
                            if audio_file:
                                st.audio(audio_file)
                                os.remove(audio_file)

    with tab2:
        st.subheader("Voice Input & Audio Recording")
        
        col1, col2 = st.columns(2)
        with col1:
            st.write("🎙️ Speech Recognition")
            voice_input = speech_component()
            
            if voice_input and voice_input != st.session_state['last_voice_input']:
                st.session_state['last_voice_input'] = voice_input
                st.markdown("**Transcribed Text:**")
                st.write(voice_input)
                
                if st.button("🔍 Search Videos"):
                    results = search.search(voice_input, num_results)
                    for i, result in enumerate(results, 1):
                        with st.expander(f"Result {i}", expanded=i==1):
                            st.write(result['description'])
                            if result['youtube_id']:
                                st.video(f"https://youtube.com/watch?v={result['youtube_id']}&t={result['start_time']}")
        
        with col2:
            st.write("🎵 Audio Recorder")
            audio_bytes = audio_recorder()
            if audio_bytes:
                process_audio_input(audio_bytes)

    with tab3:
        st.subheader("Arxiv Research")
        arxiv_query = st.text_input("🔍 Research Query:")
        
        col1, col2 = st.columns(2)
        with col1:
            vocal_summary = st.checkbox("Generate Audio Summary", value=True)
        with col2:
            extended_refs = st.checkbox("Include Extended References", value=False)
            
        if st.button("🔍 Search Arxiv") and arxiv_query:
            perform_arxiv_search(arxiv_query, vocal_summary, extended_refs)

    # Sidebar for history and settings
    with st.sidebar:
        st.subheader("⚙️ Settings & History")
        
        if st.button("🗑️ Clear History"):
            st.session_state['search_history'] = []
            st.experimental_rerun()
        
        st.markdown("### Recent Searches")
        for entry in reversed(st.session_state['search_history'][-5:]):
            st.markdown(f"**{entry['timestamp']}**: {entry['query']}")

        st.markdown("### Voice Settings")
        st.selectbox("TTS Voice:", 
                    ["en-US-AriaNeural", "en-US-GuyNeural", "en-GB-SoniaNeural"],
                    key="tts_voice")

if __name__ == "__main__":
    main()