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Update app.py
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app.py
CHANGED
@@ -1,6 +1,6 @@
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import streamlit as st
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import anthropic
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import openai
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import base64
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from datetime import datetime
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import plotly.graph_objects as go
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@@ -87,6 +87,8 @@ if "openai_model" not in st.session_state:
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st.session_state["openai_model"] = "gpt-4o-2024-05-13"
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Custom CSS
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st.markdown("""
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@@ -180,7 +182,12 @@ bike_collections = {
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}
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}
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#
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def generate_filename(prompt, file_type):
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"""Generate a safe filename using the prompt and file type."""
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central = pytz.timezone('US/Central')
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@@ -208,6 +215,39 @@ def get_download_link(file_path):
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b64 = base64.b64encode(contents).decode()
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return f'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file_path)}">Download {os.path.basename(file_path)}π</a>'
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@st.cache_resource
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def SpeechSynthesis(result):
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"""HTML5 Speech Synthesis."""
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@@ -234,6 +274,79 @@ def SpeechSynthesis(result):
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'''
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components.html(documentHTML5, width=1280, height=300)
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# Media Processing Functions
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def process_image(image_input, user_prompt):
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"""Process image with GPT-4o vision."""
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@@ -243,6 +356,8 @@ def process_image(image_input, user_prompt):
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base64_image = base64.b64encode(image_input).decode("utf-8")
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response = openai_client.chat.completions.create(
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model=st.session_state["openai_model"],
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messages=[
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@@ -279,6 +394,17 @@ def process_audio(audio_input, text_input=''):
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filename = generate_filename(transcription.text, "wav")
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create_and_save_file(audio_input, "wav", transcription.text, True)
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def process_video(video_path, seconds_per_frame=1):
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"""Process video files for frame extraction and audio."""
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base64Frames = []
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@@ -328,107 +454,13 @@ def process_video_with_gpt(video_input, user_prompt):
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return response.choices[0].message.content
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# ArXiv Search Functions
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def search_arxiv(query):
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"""Search ArXiv papers using Hugging Face client."""
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client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
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response = client.predict(
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query,
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"mistralai/Mixtral-8x7B-Instruct-v0.1",
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True,
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api_name="/ask_llm"
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)
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return response
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# Chat Processing Functions
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def process_with_gpt(text_input):
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"""Process text with GPT-4o."""
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if text_input:
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st.session_state.messages.append({"role": "user", "content": text_input})
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with st.chat_message("user"):
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st.markdown(text_input)
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with st.chat_message("assistant"):
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completion = openai_client.chat.completions.create(
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model=st.session_state["openai_model"],
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messages=[
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{"role": m["role"], "content": m["content"]}
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for m in st.session_state.messages
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],
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stream=False
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)
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return_text = completion.choices[0].message.content
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st.write("GPT-4o: " + return_text)
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filename = generate_filename(text_input, "md")
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create_file(filename, text_input, return_text)
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st.session_state.messages.append({"role": "assistant", "content": return_text})
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return return_text
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def process_with_claude(text_input):
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"""Process text with Claude."""
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if text_input:
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response = claude_client.messages.create(
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model="claude-3-sonnet-20240229",
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max_tokens=1000,
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messages=[
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{"role": "user", "content": text_input}
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]
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)
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response_text = response.content[0].text
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st.write("Claude: " + response_text)
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filename = generate_filename(text_input, "md")
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create_file(filename, text_input, response_text)
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st.session_state.chat_history.append({
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"user": text_input,
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"claude": response_text
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})
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return response_text
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# File Management Functions
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def load_file(file_name):
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"""Load file content."""
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with open(file_name, "r", encoding='utf-8') as file:
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content = file.read()
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return content
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def create_zip_of_files(files):
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"""Create zip archive of files."""
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zip_name = "all_files.zip"
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with zipfile.ZipFile(zip_name, 'w') as zipf:
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for file in files:
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zipf.write(file)
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return zip_name
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def get_media_html(media_path, media_type="video", width="100%"):
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"""Generate HTML for media player."""
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media_data = base64.b64encode(open(media_path, 'rb').read()).decode()
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if media_type == "video":
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return f'''
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<video width="{width}" controls autoplay muted loop>
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<source src="data:video/mp4;base64,{media_data}" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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'''
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else: # audio
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return f'''
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<audio controls style="width: {width};">
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<source src="data:audio/mpeg;base64,{media_data}" type="audio/mpeg">
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Your browser does not support the audio element.
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</audio>
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'''
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def create_media_gallery():
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"""Create the media gallery interface."""
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st.header("π¬ Media Gallery")
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tabs = st.tabs(["πΌοΈ Images", "π΅ Audio", "π₯ Video", "π¨ Scene Generator"])
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with tabs[0]:
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image_files = glob.glob("*.png") + glob.glob("*.jpg")
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if image_files:
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num_cols = st.slider("Number of columns", 1, 5, 3)
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st.markdown(analysis)
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SpeechSynthesis(analysis)
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with tabs[1]:
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audio_files = glob.glob("*.mp3") + glob.glob("*.wav")
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for audio_file in audio_files:
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with st.expander(f"π΅ {os.path.basename(audio_file)}"):
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st.write(transcription)
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SpeechSynthesis(transcription)
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with tabs[2]:
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video_files = glob.glob("*.mp4")
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for video_file in video_files:
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with st.expander(f"π₯ {os.path.basename(video_file)}"):
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st.markdown(analysis)
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SpeechSynthesis(analysis)
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with tabs[3]:
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for collection_name, bikes in bike_collections.items():
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st.subheader(collection_name)
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cols = st.columns(len(bikes))
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st.write(prompt)
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SpeechSynthesis(prompt)
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def display_file_manager():
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"""Display file management sidebar."""
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st.sidebar.title("π File Management")
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import streamlit as st
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import anthropic
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import openai
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import base64
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from datetime import datetime
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import plotly.graph_objects as go
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st.session_state["openai_model"] = "gpt-4o-2024-05-13"
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if "messages" not in st.session_state:
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st.session_state.messages = []
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if "search_queries" not in st.session_state:
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st.session_state.search_queries = []
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# Custom CSS
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st.markdown("""
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}
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}
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# File Operations Functions
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def create_file(filename, prompt, response, is_image=False):
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"""Basic file creation with prompt and response."""
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with open(filename, "w", encoding="utf-8") as f:
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f.write(prompt + "\n\n" + response)
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def generate_filename(prompt, file_type):
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"""Generate a safe filename using the prompt and file type."""
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central = pytz.timezone('US/Central')
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b64 = base64.b64encode(contents).decode()
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return f'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file_path)}">Download {os.path.basename(file_path)}π</a>'
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def load_file(file_name):
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"""Load file content."""
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with open(file_name, "r", encoding='utf-8') as file:
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content = file.read()
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return content
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def create_zip_of_files(files):
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"""Create zip archive of files."""
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zip_name = "all_files.zip"
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with zipfile.ZipFile(zip_name, 'w') as zipf:
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for file in files:
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zipf.write(file)
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return zip_name
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def get_media_html(media_path, media_type="video", width="100%"):
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"""Generate HTML for media player."""
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media_data = base64.b64encode(open(media_path, 'rb').read()).decode()
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if media_type == "video":
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return f'''
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<video width="{width}" controls autoplay muted loop>
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<source src="data:video/mp4;base64,{media_data}" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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'''
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else: # audio
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return f'''
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<audio controls style="width: {width};">
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<source src="data:audio/mpeg;base64,{media_data}" type="audio/mpeg">
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Your browser does not support the audio element.
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</audio>
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'''
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# Speech Synthesis
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@st.cache_resource
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def SpeechSynthesis(result):
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"""HTML5 Speech Synthesis."""
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'''
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components.html(documentHTML5, width=1280, height=300)
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# ArXiv Search Functions
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def search_arxiv(query):
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"""Search ArXiv papers using Hugging Face client."""
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start_time = time.strftime("%Y-%m-%d %H:%M:%S")
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client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
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# First query - Get papers
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response1 = client.predict(
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query,
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10,
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"Semantic Search - up to 10 Mar 2024",
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"mistralai/Mixtral-8x7B-Instruct-v0.1",
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api_name="/update_with_rag_md"
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)
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# Second query - Get summary
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response2 = client.predict(
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query,
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"mistralai/Mixtral-8x7B-Instruct-v0.1",
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True,
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api_name="/ask_llm"
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)
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Question = '### π ' + query + '\r\n'
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References = response1[0]
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References2 = response1[1]
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ReferenceLinks = extract_urls(References)
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filename = generate_filename(query, "md")
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create_file(filename, query, References + ReferenceLinks)
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results = Question + '\r\n' + response2 + '\r\n' + References + '\r\n' + ReferenceLinks
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end_time = time.strftime("%Y-%m-%d %H:%M:%S")
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start_timestamp = time.mktime(time.strptime(start_time, "%Y-%m-%d %H:%M:%S"))
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end_timestamp = time.mktime(time.strptime(end_time, "%Y-%m-%d %H:%M:%S"))
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elapsed_seconds = end_timestamp - start_timestamp
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st.write(f"Start time: {start_time}")
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st.write(f"Finish time: {end_time}")
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st.write(f"Elapsed time: {elapsed_seconds:.2f} seconds")
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return results
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def extract_urls(text):
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"""Extract URLs from ArXiv search results."""
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try:
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date_pattern = re.compile(r'### (\d{2} \w{3} \d{4})')
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abs_link_pattern = re.compile(r'\[(.*?)\]\((https://arxiv\.org/abs/\d+\.\d+)\)')
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pdf_link_pattern = re.compile(r'\[β¬οΈ\]\((https://arxiv\.org/pdf/\d+\.\d+)\)')
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title_pattern = re.compile(r'### \d{2} \w{3} \d{4} \| \[(.*?)\]')
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date_matches = date_pattern.findall(text)
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abs_link_matches = abs_link_pattern.findall(text)
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pdf_link_matches = pdf_link_pattern.findall(text)
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title_matches = title_pattern.findall(text)
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markdown_text = ""
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for i in range(len(date_matches)):
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date = date_matches[i]
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title = title_matches[i]
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abs_link = abs_link_matches[i][1]
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pdf_link = pdf_link_matches[i]
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markdown_text += f"**Date:** {date}\n\n"
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markdown_text += f"**Title:** {title}\n\n"
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markdown_text += f"**Abstract Link:** [{abs_link}]({abs_link})\n\n"
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markdown_text += f"**PDF Link:** [{pdf_link}]({pdf_link})\n\n"
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markdown_text += "---\n\n"
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return markdown_text
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except:
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st.write('Error extracting URLs')
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return ''
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# Media Processing Functions
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def process_image(image_input, user_prompt):
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"""Process image with GPT-4o vision."""
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base64_image = base64.b64encode(image_input).decode("utf-8")
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response = openai_client.chat.completions.create(
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model=st.session_state["openai_model"],
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messages=[
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filename = generate_filename(transcription.text, "wav")
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create_and_save_file(audio_input, "wav", transcription.text, True)
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def save_and_play_audio(audio_recorder):
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"""Save and play recorded audio."""
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audio_bytes = audio_recorder()
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if audio_bytes:
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filename = generate_filename("Recording", "wav")
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with open(filename, 'wb') as f:
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f.write(audio_bytes)
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st.audio(audio_bytes, format="audio/wav")
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return filename
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return None
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def process_video(video_path, seconds_per_frame=1):
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"""Process video files for frame extraction and audio."""
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base64Frames = []
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return response.choices[0].message.content
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457 |
def create_media_gallery():
|
458 |
"""Create the media gallery interface."""
|
459 |
st.header("π¬ Media Gallery")
|
460 |
|
461 |
tabs = st.tabs(["πΌοΈ Images", "π΅ Audio", "π₯ Video", "π¨ Scene Generator"])
|
462 |
|
463 |
+
with tabs[0]: # Images
|
464 |
image_files = glob.glob("*.png") + glob.glob("*.jpg")
|
465 |
if image_files:
|
466 |
num_cols = st.slider("Number of columns", 1, 5, 3)
|
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|
476 |
st.markdown(analysis)
|
477 |
SpeechSynthesis(analysis)
|
478 |
|
479 |
+
with tabs[1]: # Audio
|
480 |
audio_files = glob.glob("*.mp3") + glob.glob("*.wav")
|
481 |
for audio_file in audio_files:
|
482 |
with st.expander(f"π΅ {os.path.basename(audio_file)}"):
|
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|
487 |
st.write(transcription)
|
488 |
SpeechSynthesis(transcription)
|
489 |
|
490 |
+
with tabs[2]: # Video
|
491 |
video_files = glob.glob("*.mp4")
|
492 |
for video_file in video_files:
|
493 |
with st.expander(f"π₯ {os.path.basename(video_file)}"):
|
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|
498 |
st.markdown(analysis)
|
499 |
SpeechSynthesis(analysis)
|
500 |
|
501 |
+
with tabs[3]: # Scene Generator
|
502 |
for collection_name, bikes in bike_collections.items():
|
503 |
st.subheader(collection_name)
|
504 |
cols = st.columns(len(bikes))
|
|
|
518 |
st.write(prompt)
|
519 |
SpeechSynthesis(prompt)
|
520 |
|
521 |
+
# Chat Processing Functions
|
522 |
+
def process_with_gpt(text_input):
|
523 |
+
"""Process text with GPT-4o."""
|
524 |
+
if text_input:
|
525 |
+
st.session_state.messages.append({"role": "user", "content": text_input})
|
526 |
+
|
527 |
+
with st.chat_message("user"):
|
528 |
+
st.markdown(text_input)
|
529 |
+
|
530 |
+
with st.chat_message("assistant"):
|
531 |
+
completion = openai_client.chat.completions.create(
|
532 |
+
model=st.session_state["openai_model"],
|
533 |
+
messages=[
|
534 |
+
{"role": m["role"], "content": m["content"]}
|
535 |
+
for m in st.session_state.messages
|
536 |
+
],
|
537 |
+
stream=False
|
538 |
+
)
|
539 |
+
return_text = completion.choices[0].message.content
|
540 |
+
st.write("GPT-4o: " + return_text)
|
541 |
+
|
542 |
+
filename = generate_filename(text_input, "md")
|
543 |
+
create_file(filename, text_input, return_text)
|
544 |
+
st.session_state.messages.append({"role": "assistant", "content": return_text})
|
545 |
+
return return_text
|
546 |
+
|
547 |
+
def process_with_claude(text_input):
|
548 |
+
"""Process text with Claude."""
|
549 |
+
if text_input:
|
550 |
+
response = claude_client.messages.create(
|
551 |
+
model="claude-3-sonnet-20240229",
|
552 |
+
max_tokens=1000,
|
553 |
+
messages=[
|
554 |
+
{"role": "user", "content": text_input}
|
555 |
+
]
|
556 |
+
)
|
557 |
+
response_text = response.content[0].text
|
558 |
+
st.write("Claude: " + response_text)
|
559 |
+
|
560 |
+
filename = generate_filename(text_input, "md")
|
561 |
+
create_file(filename, text_input, response_text)
|
562 |
+
|
563 |
+
st.session_state.chat_history.append({
|
564 |
+
"user": text_input,
|
565 |
+
"claude": response_text
|
566 |
+
})
|
567 |
+
return response_text
|
568 |
+
|
569 |
def display_file_manager():
|
570 |
"""Display file management sidebar."""
|
571 |
st.sidebar.title("π File Management")
|