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RescuerOfStolenBikes

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import streamlit as st
import anthropic, openai, base64, cv2, glob, json, math, os, pytz, random, re, requests, textract, time, zipfile
import plotly.graph_objects as go
import streamlit.components.v1 as components
from datetime import datetime
from audio_recorder_streamlit import audio_recorder
from bs4 import BeautifulSoup
from collections import deque
from dotenv import load_dotenv
from gradio_client import Client
from huggingface_hub import InferenceClient
from io import BytesIO
from PIL import Image
from PyPDF2 import PdfReader
from urllib.parse import quote
from xml.etree import ElementTree as ET
from openai import OpenAI
import extra_streamlit_components as stx
from streamlit.runtime.scriptrunner import get_script_run_ctx

# 🔧 Config & Setup
st.set_page_config(
    page_title="🚲BikeAI🏆 Claude/GPT Research",
    page_icon="🚲🏆",
    layout="wide",
    initial_sidebar_state="auto",
    menu_items={
        'Get Help': 'https://huggingface.co/awacke1',
        'Report a bug': 'https://huggingface.co/spaces/awacke1',
        'About': "🚲BikeAI🏆 Claude/GPT Research AI"
    }
)
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY') or st.secrets['OPENAI_API_KEY']
anthropic_key = os.getenv("ANTHROPIC_API_KEY_3") or st.secrets["ANTHROPIC_API_KEY"]
claude_client = anthropic.Anthropic(api_key=anthropic_key)
openai_client = OpenAI(api_key=openai.api_key, organization=os.getenv('OPENAI_ORG_ID'))
HF_KEY = os.getenv('HF_KEY')
API_URL = os.getenv('API_URL')

st.session_state.setdefault('transcript_history', [])
st.session_state.setdefault('chat_history', [])
st.session_state.setdefault('openai_model', "gpt-4o-2024-05-13")
st.session_state.setdefault('messages', [])
st.session_state.setdefault('last_voice_input', "")

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

# 🔑 Common Utilities
def generate_filename(prompt, file_type="md"):
    ctz = pytz.timezone('US/Central')
    date_str = datetime.now(ctz).strftime("%m%d_%H%M")
    safe = re.sub(r'[<>:"/\\\\|?*\n]', ' ', prompt)
    safe = re.sub(r'\s+', ' ', safe).strip()[:90]
    return f"{date_str}_{safe}.{file_type}"

def create_file(filename, prompt, response):
    with open(filename, 'w', encoding='utf-8') as f:
        f.write(prompt + "\n\n" + response)

def get_download_link(file):
    with open(file, "rb") as f:
        b64 = base64.b64encode(f.read()).decode()
    return f'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file)}">📂 Download {os.path.basename(file)}</a>'

@st.cache_resource
def speech_synthesis_html(result):
    html_code = f"""
    <html><body>
    <script>
    var msg = new SpeechSynthesisUtterance("{result.replace('"', '')}");
    window.speechSynthesis.speak(msg);
    </script>
    </body></html>
    """
    components.html(html_code, height=0)

def process_image(image_path, user_prompt):
    with open(image_path, "rb") as imgf:
        image_data = imgf.read()
    b64img = base64.b64encode(image_data).decode("utf-8")
    resp = openai_client.chat.completions.create(
        model=st.session_state["openai_model"],
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": [
                {"type": "text", "text": user_prompt},
                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64img}"}}
            ]}
        ],
        temperature=0.0,
    )
    return resp.choices[0].message.content

def process_audio(audio_path):
    with open(audio_path, "rb") as f:
        transcription = openai_client.audio.transcriptions.create(model="whisper-1", file=f)
    st.session_state.messages.append({"role": "user", "content": transcription.text})
    return transcription.text

def process_video(video_path, seconds_per_frame=1):
    vid = cv2.VideoCapture(video_path)
    total = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = vid.get(cv2.CAP_PROP_FPS)
    skip = int(fps*seconds_per_frame)
    frames_b64 = []
    for i in range(0, total, skip):
        vid.set(cv2.CAP_PROP_POS_FRAMES, i)
        ret, frame = vid.read()
        if not ret: break
        _, buf = cv2.imencode(".jpg", frame)
        frames_b64.append(base64.b64encode(buf).decode("utf-8"))
    vid.release()
    return frames_b64

def process_video_with_gpt(video_path, prompt):
    frames = process_video(video_path)
    resp = openai_client.chat.completions.create(
        model=st.session_state["openai_model"],
        messages=[
            {"role":"system","content":"Analyze video frames."},
            {"role":"user","content":[
                {"type":"text","text":prompt},
                *[{"type":"image_url","image_url":{"url":f"data:image/jpeg;base64,{fr}"}} for fr in frames]
            ]}
        ]
    )
    return resp.choices[0].message.content

def search_arxiv(query):
    st.write("🔍 Searching ArXiv...")
    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    r1 = client.predict(prompt=query, llm_model_picked="mistralai/Mixtral-8x7B-Instruct-v0.1", stream_outputs=True, api_name="/ask_llm")
    st.markdown("### Mistral-8x7B-Instruct-v0.1 Result")
    st.markdown(r1)
    r2 = client.predict(prompt=query, llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2", stream_outputs=True, api_name="/ask_llm")
    st.markdown("### Mistral-7B-Instruct-v0.2 Result")
    st.markdown(r2)
    return f"{r1}\n\n{r2}"

def perform_ai_lookup(q):
    start = time.time()
    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    r = client.predict(q,20,"Semantic Search","mistralai/Mixtral-8x7B-Instruct-v0.1",api_name="/update_with_rag_md")
    refs = r[0]
    r2 = client.predict(q,"mistralai/Mixtral-8x7B-Instruct-v0.1",True,api_name="/ask_llm")
    result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
    speech_synthesis_html(r2)
    st.markdown(result)
    elapsed = time.time()-start
    st.write(f"Elapsed: {elapsed:.2f} s")
    fn = generate_filename(q,"md")
    create_file(fn,q,result)
    return result

def process_with_gpt(text):
    if not text: return
    st.session_state.messages.append({"role":"user","content":text})
    with st.chat_message("user"):
        st.markdown(text)
    with st.chat_message("assistant"):
        c = openai_client.chat.completions.create(
            model=st.session_state["openai_model"],
            messages=st.session_state.messages,
            stream=False
        )
        ans = c.choices[0].message.content
        st.write("GPT-4o: " + ans)
        create_file(generate_filename(text,"md"),text,ans)
        st.session_state.messages.append({"role":"assistant","content":ans})
        return ans

def process_with_claude(text):
    if not text: return
    with st.chat_message("user"):
        st.markdown(text)
    with st.chat_message("assistant"):
        r = claude_client.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=1000,
            messages=[{"role":"user","content":text}]
        )
        ans = r.content[0].text
        st.write("Claude: " + ans)
        create_file(generate_filename(text,"md"),text,ans)
        st.session_state.chat_history.append({"user":text,"claude":ans})
        return ans

def create_zip_of_files(files):
    zip_name = "all_files.zip"
    with zipfile.ZipFile(zip_name,'w') as z:
        for f in files: z.write(f)
    return zip_name

def get_media_html(p,typ="video",w="100%"):
    d = base64.b64encode(open(p,'rb').read()).decode()
    if typ=="video":
        return f'<video width="{w}" controls autoplay muted loop><source src="data:video/mp4;base64,{d}" type="video/mp4"></video>'
    else:
        return f'<audio controls style="width:{w};"><source src="data:audio/mpeg;base64,{d}" type="audio/mpeg"></audio>'

def create_media_gallery():
    st.header("🎬 Media Gallery")
    tabs = st.tabs(["🖼️ Images", "🎵 Audio", "🎥 Video"])
    with tabs[0]:
        imgs = glob.glob("*.png")+glob.glob("*.jpg")
        if imgs:
            c = st.slider("Cols",1,5,3)
            cols = st.columns(c)
            for i,f in enumerate(imgs):
                with cols[i%c]:
                    st.image(Image.open(f),use_container_width=True)
                    if st.button(f"👀 Analyze {os.path.basename(f)}"):
                        a = process_image(f,"Describe this image.")
                        st.markdown(a)
    with tabs[1]:
        auds = glob.glob("*.mp3")+glob.glob("*.wav")
        for a in auds:
            with st.expander(f"🎵 {os.path.basename(a)}"):
                st.markdown(get_media_html(a,"audio"),unsafe_allow_html=True)
                if st.button(f"Transcribe {os.path.basename(a)}"):
                    t = process_audio(a)
                    st.write(t)
    with tabs[2]:
        vids = glob.glob("*.mp4")
        for v in vids:
            with st.expander(f"🎥 {os.path.basename(v)}"):
                st.markdown(get_media_html(v,"video"),unsafe_allow_html=True)
                if st.button(f"Analyze {os.path.basename(v)}"):
                    a = process_video_with_gpt(v,"Describe video.")
                    st.markdown(a)

def display_file_manager():
    st.sidebar.title("📁 File Management")
    files = sorted(glob.glob("*.md"),reverse=True)
    if st.sidebar.button("🗑 Delete All"):
        for f in files: os.remove(f)
        st.experimental_rerun()
    if st.sidebar.button("⬇️ Download All"):
        z= create_zip_of_files(files)
        st.sidebar.markdown(get_download_link(z),unsafe_allow_html=True)
    for f in files:
        col1,col2,col3,col4 = st.sidebar.columns([1,3,1,1])
        with col1:
            if st.button("🌐",key="v"+f):
                st.session_state.current_file=f
                c=open(f,'r',encoding='utf-8').read()
                st.write(c)
        with col2:
            st.markdown(get_download_link(f),unsafe_allow_html=True)
        with col3:
            if st.button("📂",key="e"+f):
                st.session_state.current_file=f
                st.session_state.file_content=open(f,'r',encoding='utf-8').read()
        with col4:
            if st.button("🗑",key="d"+f):
                os.remove(f)
                st.experimental_rerun()

def main():
    st.sidebar.markdown("### 🚲BikeAI🏆 Multi-Agent Research AI")
    tab_main = st.radio("Action:",["🎤 Voice Input","📸 Media Gallery","🔍 Search ArXiv","📝 File Editor"],horizontal=True)
    model_choice = st.sidebar.radio("AI Model:",["GPT+Claude+Arxiv","GPT-4o","Claude-3"])

    # Speech-to-Text component placeholder (example)
    mycomponent = components.declare_component("mycomponent", path="mycomponent")
    val = mycomponent(my_input_value="Hello")
    if val:
        user_input = val
        if model_choice == "GPT-4o":
            process_with_gpt(user_input)
        elif model_choice == "Claude-3":
            process_with_claude(user_input)
        else:
            col1,col2,col3=st.columns(3)
            with col1:
                st.subheader("GPT-4o Omni:")
                try: process_with_gpt(user_input)
                except: st.write('GPT 4o error')
            with col2:
                st.subheader("Claude-3 Sonnet:")
                try: process_with_claude(user_input)
                except: st.write('Claude error')
            with col3:
                st.subheader("Arxiv + Mistral:")
                try:
                    r = perform_ai_lookup(user_input)
                    st.markdown(r)
                except:
                    st.write("Arxiv error")

    if tab_main == "🎤 Voice Input":
        st.subheader("🎤 Voice Recognition")
        user_text = st.text_area("Message:", height=100)
        if st.button("Send 📨"):
            if user_text:
                if model_choice == "GPT-4o":
                    process_with_gpt(user_text)
                elif model_choice == "Claude-3":
                    process_with_claude(user_text)
                else:
                    col1,col2,col3=st.columns(3)
                    with col1:
                        st.subheader("GPT-4o Omni:")
                        process_with_gpt(user_text)
                    with col2:
                        st.subheader("Claude-3 Sonnet:")
                        process_with_claude(user_text)
                    with col3:
                        st.subheader("Arxiv & Mistral:")
                        res = perform_ai_lookup(user_text)
                        st.markdown(res)
        st.subheader("📜 Chat History")
        t1,t2=st.tabs(["Claude History","GPT-4o History"])
        with t1:
            for c in st.session_state.chat_history:
                st.write("**You:**", c["user"])
                st.write("**Claude:**", c["claude"])
        with t2:
            for m in st.session_state.messages:
                with st.chat_message(m["role"]):
                    st.markdown(m["content"])

    elif tab_main == "📸 Media Gallery":
        create_media_gallery()

    elif tab_main == "🔍 Search ArXiv":
        q=st.text_input("Research query:")
        if q:
            r=search_arxiv(q)
            st.markdown(r)

    elif tab_main == "📝 File Editor":
        if getattr(st.session_state,'current_file',None):
            st.subheader(f"Editing: {st.session_state.current_file}")
            new_text = st.text_area("Content:", st.session_state.file_content, height=300)
            if st.button("Save"):
                with open(st.session_state.current_file,'w',encoding='utf-8') as f:
                    f.write(new_text)
                st.success("Updated!")

    display_file_manager()

if __name__=="__main__":
    main()