Update app.py

app.py

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+import streamlit as st
+import anthropic
+import openai
+import base64
+from datetime import datetime
+import plotly.graph_objects as go
+import cv2
+import glob
+import json
+import math
+import os
+import pytz
+import random
+import re
+import requests
+import streamlit.components.v1 as components
+import textract
+import time
+import zipfile
+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, handle_file
+from huggingface_hub import InferenceClient
+from io import BytesIO
+from moviepy.editor import VideoFileClip
+from PIL import Image
+from PyPDF2 import PdfReader
+from urllib.parse import quote
+from xml.etree import ElementTree as ET
+from openai import OpenAI
+
+# 1. Configuration and Setup
+Site_Name = '🤖🧠Claude35📝🔬'
+title = "🤖🧠Claude35📝🔬"
+helpURL = 'https://huggingface.co/awacke1'
+bugURL = 'https://huggingface.co/spaces/awacke1'
+icons = '🤖🧠🔬📝'
+
+st.set_page_config(
+    page_title=title,
+    page_icon=icons,
+    layout="wide",
+    initial_sidebar_state="auto",
+    menu_items={
+        'Get Help': helpURL,
+        'Report a bug': bugURL,
+        'About': title
+    }
+)
+
+# 2. Load environment variables and initialize clients
+load_dotenv()
+
+# OpenAI setup
+openai.api_key = os.getenv('OPENAI_API_KEY')
+if openai.api_key == None:
+    openai.api_key = st.secrets['OPENAI_API_KEY']
+
+openai_client = OpenAI(
+    api_key=os.getenv('OPENAI_API_KEY'),
+    organization=os.getenv('OPENAI_ORG_ID')
+)
+
+# Claude setup
+anthropic_key = os.getenv("ANTHROPIC_API_KEY_3")
+if anthropic_key == None:
+    anthropic_key = st.secrets["ANTHROPIC_API_KEY"]
+claude_client = anthropic.Anthropic(api_key=anthropic_key)
+
+# HuggingFace setup
+API_URL = os.getenv('API_URL')
+HF_KEY = os.getenv('HF_KEY')
+MODEL1 = "meta-llama/Llama-2-7b-chat-hf"
+MODEL2 = "openai/whisper-small.en"
+
+headers = {
+    "Authorization": f"Bearer {HF_KEY}",
+    "Content-Type": "application/json"
+}
+
+# Initialize session states
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+if "openai_model" not in st.session_state:
+    st.session_state["openai_model"] = "gpt-4o-2024-05-13"
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main {
+        background: linear-gradient(to right, #1a1a1a, #2d2d2d);
+        color: #ffffff;
+    }
+    .stMarkdown {
+        font-family: 'Helvetica Neue', sans-serif;
+    }
+    .category-header {
+        background: linear-gradient(45deg, #2b5876, #4e4376);
+        padding: 20px;
+        border-radius: 10px;
+        margin: 10px 0;
+    }
+    .scene-card {
+        background: rgba(0,0,0,0.3);
+        padding: 15px;
+        border-radius: 8px;
+        margin: 10px 0;
+        border: 1px solid rgba(255,255,255,0.1);
+    }
+    .media-gallery {
+        display: grid;
+        gap: 1rem;
+        padding: 1rem;
+    }
+    .bike-card {
+        background: rgba(255,255,255,0.05);
+        border-radius: 10px;
+        padding: 15px;
+        transition: transform 0.3s;
+    }
+    .bike-card:hover {
+        transform: scale(1.02);
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Bike Collections
+bike_collections = {
+    "Celestial Collection 🌌": {
+        "Eclipse Vaulter": {
+            "prompt": """Cinematic shot of a sleek black mountain bike silhouetted against a total solar eclipse. 
+                     The corona creates an ethereal halo effect, with lens flares accentuating key points of the frame.
+                     Dynamic composition shows the bike mid-leap, with stardust particles trailing behind.
+                     Camera angle: Low angle, wide shot
+                     Lighting: Dramatic rim lighting from eclipse
+                     Color palette: Deep purples, cosmic blues, corona gold""",
+            "emoji": "🌑"
+        },
+        "Starlight Leaper": {
+            "prompt": """A black bike performing an epic leap under a vast Milky Way galaxy.
+                     Shimmering stars blanket the sky while the bike's wheels leave a trail of stardust.
+                     Camera angle: Wide-angle upward shot
+                     Lighting: Natural starlight with subtle rim lighting
+                     Color palette: Deep blues, silver highlights, cosmic purples""",
+            "emoji": "✨"
+        },
+        "Moonlit Hopper": {
+            "prompt": """A sleek black bike mid-hop over a moonlit meadow,
+                     the full moon illuminating the misty surroundings. Fireflies dance around the bike,
+                     and soft shadows create a serene yet dynamic atmosphere.
+                     Camera angle: Side profile with slight low angle
+                     Lighting: Soft moonlight with atmospheric fog
+                     Color palette: Silver blues, soft whites, deep shadows""",
+            "emoji": "🌙"
+        }
+    },
+    "Nature-Inspired Collection 🌲": {
+        "Shadow Grasshopper": {
+            "prompt": """A black bike jumping between forest paths,
+                     with dappled sunlight streaming through the canopy. Shadows dance on the bike's frame
+                     as it soars above mossy logs.
+                     Camera angle: Through-the-trees tracking shot
+                     Lighting: Natural forest lighting with sun rays
+                     Color palette: Forest greens, golden sunlight, deep shadows""",
+            "emoji": "🦗"
+        },
+        "Onyx Leapfrog": {
+            "prompt": """A bike with obsidian-black finish jumping over a sparkling creek,
+                     the reflection on the water broken into ripples by the leap. The surrounding forest
+                     is vibrant with greens and browns.
+                     Camera angle: Low angle from water level
+                     Lighting: Golden hour side lighting
+                     Color palette: Deep blacks, water blues, forest greens""",
+            "emoji": "🐸"
+        }
+    }
+}
+
+# Helper Functions
+def generate_filename(prompt, file_type):
+    """Generate a safe filename using the prompt and file type."""
+    central = pytz.timezone('US/Central')
+    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
+    replaced_prompt = re.sub(r'[<>:"/\\|?*\n]', ' ', prompt)
+    safe_prompt = re.sub(r'\s+', ' ', replaced_prompt).strip()[:240]
+    return f"{safe_date_time}_{safe_prompt}.{file_type}"
+
+
+
+
+# Function to create and save a file (and avoid the black hole of lost data 🕳)
+def create_file(filename, prompt, response, should_save=True):
+    if not should_save:
+        return
+    with open(filename, 'w', encoding='utf-8') as file:
+        file.write(prompt + "\n\n" + response)
+
+
+
+def create_and_save_file(content, file_type="md", prompt=None, is_image=False, should_save=True):
+    """Create and save file with proper handling of different types."""
+    if not should_save:
+        return None
+    filename = generate_filename(prompt if prompt else content, file_type)
+    with open(filename, "w", encoding="utf-8") as f:
+        if is_image:
+            f.write(content)
+        else:
+            f.write(prompt + "\n\n" + content if prompt else content)
+    return filename
+
+def get_download_link(file_path):
+    """Create download link for file."""
+    with open(file_path, "rb") as file:
+        contents = file.read()
+    b64 = base64.b64encode(contents).decode()
+    return f'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file_path)}">Download {os.path.basename(file_path)}📂</a>'
+
+@st.cache_resource
+def SpeechSynthesis(result):
+    """HTML5 Speech Synthesis."""
+    documentHTML5 = f'''
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>Read It Aloud</title>
+        <script type="text/javascript">
+            function readAloud() {{
+                const text = document.getElementById("textArea").value;
+                const speech = new SpeechSynthesisUtterance(text);
+                window.speechSynthesis.speak(speech);
+            }}
+        </script>
+    </head>
+    <body>
+        <h1>🔊 Read It Aloud</h1>
+        <textarea id="textArea" rows="10" cols="80">{result}</textarea>
+        <br>
+        <button onclick="readAloud()">🔊 Read Aloud</button>
+    </body>
+    </html>
+    '''
+    components.html(documentHTML5, width=1280, height=300)
+
+# Media Processing Functions
+def process_image(image_input, user_prompt):
+    """Process image with GPT-4o vision."""
+    if isinstance(image_input, str):
+        with open(image_input, "rb") as image_file:
+            image_input = image_file.read()
+            
+    base64_image = base64.b64encode(image_input).decode("utf-8")
+    
+    response = openai_client.chat.completions.create(
+        model=st.session_state["openai_model"],
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant that responds in Markdown."},
+            {"role": "user", "content": [
+                {"type": "text", "text": user_prompt},
+                {"type": "image_url", "image_url": {
+                    "url": f"data:image/png;base64,{base64_image}"
+                }}
+            ]}
+        ],
+        temperature=0.0,
+    )
+    
+    return response.choices[0].message.content
+
+def process_audio(audio_input, text_input=''):
+    """Process audio with Whisper and GPT."""
+    if isinstance(audio_input, str):
+        with open(audio_input, "rb") as file:
+            audio_input = file.read()
+
+    transcription = openai_client.audio.transcriptions.create(
+        model="whisper-1",
+        file=audio_input,
+    )
+    
+    st.session_state.messages.append({"role": "user", "content": transcription.text})
+    
+    with st.chat_message("assistant"):
+        st.markdown(transcription.text)
+        SpeechSynthesis(transcription.text)
+        
+        filename = generate_filename(transcription.text, "wav")
+        create_and_save_file(audio_input, "wav", transcription.text, True)
+
+def process_video(video_path, seconds_per_frame=1):
+    """Process video files for frame extraction and audio."""
+    base64Frames = []
+    video = cv2.VideoCapture(video_path)
+    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = video.get(cv2.CAP_PROP_FPS)
+    frames_to_skip = int(fps * seconds_per_frame)
+    
+    for frame_idx in range(0, total_frames, frames_to_skip):
+        video.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
+        success, frame = video.read()
+        if not success:
+            break
+        _, buffer = cv2.imencode(".jpg", frame)
+        base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
+    
+    video.release()
+    
+    # Extract audio
+    base_video_path = os.path.splitext(video_path)[0]
+    audio_path = f"{base_video_path}.mp3"
+    try:
+        video_clip = VideoFileClip(video_path)
+        video_clip.audio.write_audiofile(audio_path)
+        video_clip.close()
+    except:
+        st.warning("No audio track found in video")
+        audio_path = None
+    
+    return base64Frames, audio_path
+
+def process_video_with_gpt(video_input, user_prompt):
+    """Process video with GPT-4o vision."""
+    base64Frames, audio_path = process_video(video_input)
+    
+    response = openai_client.chat.completions.create(
+        model=st.session_state["openai_model"],
+        messages=[
+            {"role": "system", "content": "Analyze the video frames and provide a detailed description."},
+            {"role": "user", "content": [
+                {"type": "text", "text": user_prompt},
+                *[{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{frame}"}}
+                  for frame in base64Frames]
+            ]}
+        ]
+    )
+    
+    return response.choices[0].message.content
+
+# ArXiv Search Functions
+def search_arxiv(query):
+    """Search ArXiv papers using Hugging Face client."""
+    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+    response = client.predict(
+        query,
+        "mistralai/Mixtral-8x7B-Instruct-v0.1",
+        True,
+        api_name="/ask_llm"
+    )
+    return response
+
+# Chat Processing Functions
+def process_with_gpt(text_input):
+    """Process text with GPT-4o."""
+    if text_input:
+        st.session_state.messages.append({"role": "user", "content": text_input})
+        
+        with st.chat_message("user"):
+            st.markdown(text_input)
+        
+        with st.chat_message("assistant"):
+            completion = openai_client.chat.completions.create(
+                model=st.session_state["openai_model"],
+                messages=[
+                    {"role": m["role"], "content": m["content"]}
+                    for m in st.session_state.messages
+                ],
+                stream=False
+            )
+            return_text = completion.choices[0].message.content
+            st.write("GPT-4o: " + return_text)
+            
+            filename = generate_filename(text_input, "md")
+            create_file(filename, text_input, return_text)
+            st.session_state.messages.append({"role": "assistant", "content": return_text})
+            return return_text
+
+def process_with_claude(text_input):
+    """Process text with Claude."""
+    if text_input:
+        response = claude_client.messages.create(
+            model="claude-3-sonnet-20240229",
+            max_tokens=1000,
+            messages=[
+                {"role": "user", "content": text_input}
+            ]
+        )
+        response_text = response.content[0].text
+        st.write("Claude: " + response_text)
+        
+        filename = generate_filename(text_input, "md")
+        create_file(filename, text_input, response_text)
+        
+        st.session_state.chat_history.append({
+            "user": text_input,
+            "claude": response_text
+        })
+        return response_text
+
+# File Management Functions
+def load_file(file_name):
+    """Load file content."""
+    with open(file_name, "r", encoding='utf-8') as file:
+        content = file.read()
+    return content
+
+def create_zip_of_files(files):
+    """Create zip archive of files."""
+    zip_name = "all_files.zip"
+    with zipfile.ZipFile(zip_name, 'w') as zipf:
+        for file in files:
+            zipf.write(file)
+    return zip_name
+
+
+
+def get_media_html(media_path, media_type="video", width="100%"):
+    """Generate HTML for media player."""
+    media_data = base64.b64encode(open(media_path, 'rb').read()).decode()
+    if media_type == "video":
+        return f'''
+        <video width="{width}" controls autoplay muted loop>
+            <source src="data:video/mp4;base64,{media_data}" type="video/mp4">
+            Your browser does not support the video tag.
+        </video>
+        '''
+    else:  # audio
+        return f'''
+        <audio controls style="width: {width};">
+            <source src="data:audio/mpeg;base64,{media_data}" type="audio/mpeg">
+            Your browser does not support the audio element.
+        </audio>
+        '''
+
+def create_media_gallery():
+    """Create the media gallery interface."""
+    st.header("🎬 Media Gallery")
+    
+    tabs = st.tabs(["🖼️ Images", "🎵 Audio", "🎥 Video", "🎨 Scene Generator"])
+    
+    with tabs[0]:
+        image_files = glob.glob("*.png") + glob.glob("*.jpg")
+        if image_files:
+            num_cols = st.slider("Number of columns", 1, 5, 3)
+            cols = st.columns(num_cols)
+            for idx, image_file in enumerate(image_files):
+                with cols[idx % num_cols]:
+                    img = Image.open(image_file)
+                    st.image(img, use_container_width=True)
+                    
+                    # Add GPT vision analysis option
+                    if st.button(f"Analyze {os.path.basename(image_file)}"):
+                        analysis = process_image(image_file, 
+                                              "Describe this image in detail and identify key elements.")
+                        st.markdown(analysis)
+    
+    with tabs[1]:
+        audio_files = glob.glob("*.mp3") + glob.glob("*.wav")
+        for audio_file in audio_files:
+            with st.expander(f"🎵 {os.path.basename(audio_file)}"):
+                st.markdown(get_media_html(audio_file, "audio"), unsafe_allow_html=True)
+                if st.button(f"Transcribe {os.path.basename(audio_file)}"):
+                    with open(audio_file, "rb") as f:
+                        transcription = process_audio(f)
+                        st.write(transcription)
+    
+    with tabs[2]:
+        video_files = glob.glob("*.mp4")
+        for video_file in video_files:
+            with st.expander(f"🎥 {os.path.basename(video_file)}"):
+                st.markdown(get_media_html(video_file, "video"), unsafe_allow_html=True)
+                if st.button(f"Analyze {os.path.basename(video_file)}"):
+                    analysis = process_video_with_gpt(video_file, 
+                                                    "Describe what's happening in this video.")
+                    st.markdown(analysis)
+    
+    with tabs[3]:
+        for collection_name, bikes in bike_collections.items():
+            st.subheader(collection_name)
+            cols = st.columns(len(bikes))
+            
+            for idx, (bike_name, details) in enumerate(bikes.items()):
+                with cols[idx]:
+                    st.markdown(f"""
+                    <div class='bike-card'>
+                        <h3>{details['emoji']} {bike_name}</h3>
+                        <p>{details['prompt']}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+                    
+                    if st.button(f"Generate {bike_name} Scene"):
+                        prompt = details['prompt']
+                        # Here you could integrate with image generation API
+                        st.write(f"Generated scene description for {bike_name}:")
+                        st.write(prompt)
+
+def display_file_manager():
+    """Display file management sidebar."""
+    st.sidebar.title("📁 File Management")
+    
+    all_files = glob.glob("*.md")
+    all_files.sort(reverse=True)
+
+    if st.sidebar.button("🗑 Delete All"):
+        for file in all_files:
+            os.remove(file)
+        st.rerun()
+
+    if st.sidebar.button("⬇️ Download All"):
+        zip_file = create_zip_of_files(all_files)
+        st.sidebar.markdown(get_download_link(zip_file), unsafe_allow_html=True)
+
+    for file in all_files:
+        col1, col2, col3, col4 = st.sidebar.columns([1,3,1,1])
+        with col1:
+            if st.button("🌐", key="view_"+file):
+                st.session_state.current_file = file
+                st.session_state.file_content = load_file(file)
+        with col2:
+            st.markdown(get_download_link(file), unsafe_allow_html=True)
+        with col3:
+            if st.button("📂", key="edit_"+file):
+                st.session_state.current_file = file
+                st.session_state.file_content = load_file(file)
+        with col4:
+            if st.button("🗑", key="delete_"+file):
+                os.remove(file)
+                st.rerun()
+
+def main():
+    st.title("🚲 Bike Cinematic Universe & AI Assistant")
+    
+    # Main navigation
+    tab_main = st.radio("Choose Action:", 
+                        ["💬 Chat", "📸 Media Gallery", "🔍 Search ArXiv", "📝 File Editor"],
+                        horizontal=True)
+    
+    if tab_main == "💬 Chat":
+        # Model Selection
+        model_choice = st.sidebar.radio(
+            "Choose AI Model:",
+            ["GPT-4o", "Claude-3", "Both"]
+        )
+        
+        # Chat Interface
+        user_input = st.text_area("Message:", height=100)
+        
+        if st.button("Send 📨"):
+            if user_input:
+                if model_choice == "GPT-4o":
+                    gpt_response = process_with_gpt(user_input)
+                elif model_choice == "Claude-3":
+                    claude_response = process_with_claude(user_input)
+                else:  # Both
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        st.subheader("GPT-4o Response")
+                        gpt_response = process_with_gpt(user_input)
+                    with col2:
+                        st.subheader("Claude-3 Response")
+                        claude_response = process_with_claude(user_input)
+        
+        # Display Chat History
+        st.subheader("Chat History 📜")
+        tab1, tab2 = st.tabs(["Claude History", "GPT-4o History"])
+        
+        with tab1:
+            for chat in st.session_state.chat_history:
+                st.text_area("You:", chat["user"], height=100, disabled=True)
+                st.text_area("Claude:", chat["claude"], height=200, disabled=True)
+                st.markdown("---")
+        
+        with tab2:
+            for message in st.session_state.messages:
+                with st.chat_message(message["role"]):
+                    st.markdown(message["content"])
+    
+    elif tab_main == "📸 Media Gallery":
+        create_media_gallery()
+    
+    elif tab_main == "🔍 Search ArXiv":
+        query = st.text_input("Enter your research query:")
+        if query:
+            with st.spinner("Searching ArXiv..."):
+                results = search_arxiv(query)
+                st.markdown(results)
+    
+    elif tab_main == "📝 File Editor":
+        if hasattr(st.session_state, 'current_file'):
+            st.subheader(f"Editing: {st.session_state.current_file}")
+            new_content = st.text_area("Content:", st.session_state.file_content, height=300)
+            if st.button("Save Changes"):
+                with open(st.session_state.current_file, 'w', encoding='utf-8') as file:
+                    file.write(new_content)
+                st.success("File updated successfully!")
+
+    # Always show file manager in sidebar
+    display_file_manager()
+
+if __name__ == "__main__":
+    main()