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RescuerOfStolenBikes

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awacke1 commited on 15 days ago

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Create app.py

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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
+import asyncio
+import edge_tts  # ensure this is installed (pip install edge-tts)
+# 🔧 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)
+#------------add EdgeTTS
+# --- NEW FUNCTIONS FOR EDGE TTS ---
+async def edge_tts_generate_audio(text, voice="en-US-AriaNeural", rate=0, pitch=0):
+    if not text.strip():
+        return None
+    rate_str = f"{rate:+d}%"
+    pitch_str = f"{pitch:+d}Hz"
+    communicate = edge_tts.Communicate(text, voice, rate=rate_str, pitch=pitch_str)
+    out_fn = generate_filename(text,"mp3")
+    await communicate.save(out_fn)
+    return out_fn
+def speak_with_edge_tts(text, voice="en-US-AriaNeural", rate=0, pitch=0):
+    return asyncio.run(edge_tts_generate_audio(text, voice, rate, pitch))
+def play_and_download_audio(file_path):
+    if file_path and os.path.exists(file_path):
+        st.audio(file_path)
+        st.markdown(get_download_link(file_path), unsafe_allow_html=True)
+#---------------------------
+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, vocal_summary=True, extended_refs=False, titles_summary=True):
+    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}"
+    st.markdown(result)
+    # Main Vocal Summary (Short Answer)
+    if vocal_summary:
+        start_main_part = time.time()
+        audio_file_main = speak_with_edge_tts(r2, voice="en-US-AriaNeural", rate=0, pitch=0)
+        st.write("### 🎙️ Vocal Summary (Short Answer)")
+        play_and_download_audio(audio_file_main)
+        st.write(f"**Elapsed (Short Answer):** {time.time() - start_main_part:.2f} s")
+    # Extended References & Summaries (optional)
+    if extended_refs:
+        start_refs_part = time.time()
+        summaries_text = "Here are the summaries from the references: " + refs.replace('"','')
+        audio_file_refs = speak_with_edge_tts(summaries_text, voice="en-US-AriaNeural", rate=0, pitch=0)
+        st.write("### 📜 Extended References & Summaries")
+        play_and_download_audio(audio_file_refs)
+        st.write(f"**Elapsed (Extended References):** {time.time() - start_refs_part:.2f} s")
+    # Paper Titles Only (short)
+    if titles_summary:
+        start_titles_part = time.time()
+        titles = []
+        for line in refs.split('\n'):
+            m = re.search(r"\[([^\]]+)\]", line)
+            if m:
+                titles.append(m.group(1))
+        if titles:
+            titles_text = "Here are the titles of the papers: " + ", ".join(titles)
+            audio_file_titles = speak_with_edge_tts(titles_text, voice="en-US-AriaNeural", rate=0, pitch=0)
+            st.write("### 🔖 Paper Titles")
+            play_and_download_audio(audio_file_titles)
+            st.write(f"**Elapsed (Titles):** {time.time() - start_titles_part:.2f} s")
+    elapsed = time.time()-start
+    st.write(f"**Total 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():
+    md_files = glob.glob("*.md")
+    mp3_files = glob.glob("*.mp3")
+    all_files = md_files + mp3_files
+    zip_name = "all_files.zip"
+    with zipfile.ZipFile(zip_name,'w') as z:
+        for f in all_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>'
+# CHANGES START HERE:
+# Define file emojis and the directory to scan
+FILE_EMOJIS = {
+    "cards": "🗃️",
+    "csv": "📊",
+    "heic": "🖼️",
+    "ico": "🪧",
+    "jpeg": "🖼️",
+    "json": "🔧",
+    "md": "📝",
+    "mid": "🎼",
+    "mov": "🎥",
+    "mp3": "🎵",
+    "mp4": "🎞️",
+    "png": "🖼️",
+    "svg": "🖌️",
+    "txt": "📄",
+    "wav": "🎶",
+    "webm": "📽️",
+    "webp": "🏞️",
+    "zip": "📦",
+}
+MEDIA_DIR = "Media"
+def get_file_ext(filename):
+    return os.path.splitext(filename)[1].lower().strip('.')
+def load_files():
+    all_files = []
+    for root, dirs, files in os.walk(MEDIA_DIR):
+        for f in files:
+            fp = os.path.join(root, f)
+            if os.path.isfile(fp):
+                ext = get_file_ext(fp)
+                mod_time = os.path.getmtime(fp)
+                all_files.append((fp, ext, mod_time))
+    return all_files
+def display_files_sidebar():
+    st.sidebar.title("📂 Media Files")
+    all_files = load_files()
+    from collections import defaultdict
+    ext_map = defaultdict(list)
+    for fp, ext, mod_time in all_files:
+        ext_map[ext].append((fp, mod_time))
+    # Sort files in each extension group by modification time descending
+    for ext in ext_map:
+        ext_map[ext].sort(key=lambda x: x[1], reverse=True)
+    # Sort extensions by number of files descending
+    sorted_ext = sorted(ext_map.keys(), key=lambda x: len(ext_map[x]), reverse=True)
+    for ext in sorted_ext:
+        emoji = FILE_EMOJIS.get(ext, "📁")
+        count = len(ext_map[ext])
+        with st.sidebar.expander(f"{emoji} {ext.upper()} ({count})"):
+            for fp, mod_time in ext_map[ext]:
+                basename = os.path.basename(fp)
+                last_mod = datetime.fromtimestamp(mod_time).strftime("%Y-%m-%d %H:%M:%S")
+                col1, col2 = st.columns([3,1])
+                with col1:
+                    st.write(f"**{basename}** - Modified: {last_mod}")
+                with col2:
+                    if ext == "mp3":
+                        # For MP3, load download link only after user clicks an expander
+                        mp3_exp = st.expander("Load MP3 Download Link")
+                        with mp3_exp:
+                            st.markdown(get_download_link(fp), unsafe_allow_html=True)
+                            # If desired, add an on-demand audio player here.
+                    else:
+                        # Direct download link for other files
+                        st.markdown(get_download_link(fp), unsafe_allow_html=True)
+# CHANGES END HERE
+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:", ["Arxiv","GPT-4o","Claude-3","GPT+Claude+Arxiv"], index=0)
+    # Declare the component
+    mycomponent = components.declare_component("mycomponent", path="mycomponent")
+    val = mycomponent(my_input_value="Hello")
+    if val:
+        user_input = val.strip()
+        if user_input:
+            if model_choice == "GPT-4o":
+                process_with_gpt(user_input)
+            elif model_choice == "Claude-3":
+                process_with_claude(user_input)
+            elif model_choice == "Arxiv":
+                st.subheader("Arxiv Only Results:")
+                perform_ai_lookup(user_input, vocal_summary=True, extended_refs=False, titles_summary=True)
+            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:
+                        perform_ai_lookup(user_input, vocal_summary=True, extended_refs=False, titles_summary=True)
+                    except:
+                        st.write("Arxiv error")
+    if tab_main == "🔍 Search ArXiv":
+        st.subheader("🔍 Search ArXiv")
+        q=st.text_input("Research query:")
+        # 🎛️ Audio Generation Options
+        st.markdown("### 🎛️ Audio Generation Options")
+        vocal_summary = st.checkbox("🎙️ Vocal Summary (Short Answer)", value=True)
+        extended_refs = st.checkbox("📜 Extended References & Summaries (Long)", value=False)
+        titles_summary = st.checkbox("🔖 Paper Titles Only", value=True)
+        if q:
+            q = q.strip()
+            if q and st.button("Run ArXiv Query"):
+                r = perform_ai_lookup(q, vocal_summary=vocal_summary, extended_refs=extended_refs, titles_summary=titles_summary)
+                st.markdown(r)
+    elif tab_main == "🎤 Voice Input":
+        st.subheader("🎤 Voice Recognition")
+        user_text = st.text_area("Message:", height=100)
+        user_text = user_text.strip()
+        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)
+                elif model_choice == "Arxiv":
+                    st.subheader("Arxiv Only Results:")
+                    perform_ai_lookup(user_text, vocal_summary=True, extended_refs=False, titles_summary=True)
+                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, vocal_summary=True, extended_refs=False, titles_summary=True)
+                        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":
+        st.header("🎬 Media Gallery - Images and Videos")
+        tabs = st.tabs(["🖼️ Images", "🎥 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)
+            else:
+                st.write("No images found.")
+        with tabs[1]:
+            vids = glob.glob("*.mp4")
+            if vids:
+                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)
+            else:
+                st.write("No videos found.")
+    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!")
+        else:
+            st.write("Select a file from the sidebar to edit.")
+    # Use the new sidebar display function
+    display_files_sidebar()
+if __name__=="__main__":
+    main()