Update app.py
Browse files
app.py
CHANGED
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@@ -79,6 +79,10 @@ def load_file(file_name):
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with open(file_name, "r") as file:
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content = file.read()
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return content
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#import streamlit as st
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#from gradio_client import Client
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#client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
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@@ -91,6 +95,42 @@ def load_file(file_name):
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#)
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#st.markdown(result)
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def search_arxiv(query):
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st.title("▶️ Semantic and Episodic Memory System")
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@@ -112,10 +152,18 @@ def search_arxiv(query):
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)
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# Show ArXiv Scholary Articles! ----------------*************-------------***************----------------------------------------
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st.markdown(result)
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-
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SpeechSynthesis(result) # Search History Reader / Writer IO Memory - Audio at Same time as Reading.
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filename=generate_filename(result, "md")
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base_filename, ext = os.path.splitext(filename)
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with open(f"{base_filename}.md", 'w') as file:
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@@ -137,7 +185,6 @@ def search_arxiv(query):
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create_file(filename, query, result, should_save)
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# Show ArXiv Scholary Articles! ----------------*************-------------***************----------------------------------------
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file_type = st.radio("Select Which Type of Memory You Prefer:", ("Semantic", "Episodic"))
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@@ -362,36 +409,6 @@ roleplaying_glossary = {
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# HTML5 based Speech Synthesis (Text to Speech in Browser)
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@st.cache_resource
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def SpeechSynthesis(result):
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documentHTML5='''
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<!DOCTYPE html>
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<html>
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<head>
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<title>Read It Aloud</title>
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<script type="text/javascript">
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function readAloud() {
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const text = document.getElementById("textArea").value;
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const speech = new SpeechSynthesisUtterance(text);
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window.speechSynthesis.speak(speech);
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}
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</script>
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</head>
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<body>
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<h1>🔊 Read It Aloud</h1>
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<textarea id="textArea" rows="10" cols="80">
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'''
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documentHTML5 = documentHTML5 + result
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documentHTML5 = documentHTML5 + '''
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</textarea>
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<br>
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<button onclick="readAloud()">🔊 Read Aloud</button>
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</body>
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</html>
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'''
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components.html(documentHTML5, width=1280, height=300)
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@st.cache_resource
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def get_table_download_link(file_path):
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@@ -1378,7 +1395,7 @@ What is SORA?
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#example_input = st.text_input("Enter your prompt text:", value=prompt, help="Enter text to get a response.")
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#example_input = st.text_area("Enter Prompt :", '', height=100
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# Search History
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session_state = {}
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if "search_queries" not in session_state:
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session_state["search_queries"] = []
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@@ -1388,12 +1405,10 @@ What is SORA?
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# Search AI
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query=example_input
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except:
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st.markdown(' ')
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st.write("Search history:")
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for example_input in session_state["search_queries"]:
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with open(file_name, "r") as file:
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content = file.read()
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return content
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+
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#import streamlit as st
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#from gradio_client import Client
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#client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
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#)
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#st.markdown(result)
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# HTML5 based Speech Synthesis (Text to Speech in Browser)
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@st.cache_resource
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def SpeechSynthesis(result):
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documentHTML5='''
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<!DOCTYPE html>
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<html>
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<head>
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<title>Read It Aloud</title>
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<script type="text/javascript">
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function readAloud() {
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const text = document.getElementById("textArea").value;
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const speech = new SpeechSynthesisUtterance(text);
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window.speechSynthesis.speak(speech);
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}
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</script>
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</head>
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<body>
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<h1>🔊 Read It Aloud</h1>
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<textarea id="textArea" rows="10" cols="80">
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'''
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documentHTML5 = documentHTML5 + result
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documentHTML5 = documentHTML5 + '''
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</textarea>
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<br>
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<button onclick="readAloud()">🔊 Read Aloud</button>
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</body>
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</html>
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'''
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components.html(documentHTML5, width=1280, height=300)
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def search_arxiv(query):
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st.title("▶️ Semantic and Episodic Memory System")
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)
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# Show ArXiv Scholary Articles! ----------------*************-------------***************----------------------------------------
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st.markdown(result)
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arxiv_results = st.text_area("ArXiv Results: ", value=result, height=700)
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SpeechSynthesis(result) # Search History Reader / Writer IO Memory - Audio at Same time as Reading.
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# Show ArXiv Scholary Articles! ----------------*************-------------***************----------------------------------------
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filename=generate_filename(result, "md")
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base_filename, ext = os.path.splitext(filename)
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with open(f"{base_filename}.md", 'w') as file:
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create_file(filename, query, result, should_save)
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file_type = st.radio("Select Which Type of Memory You Prefer:", ("Semantic", "Episodic"))
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@st.cache_resource
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def get_table_download_link(file_path):
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#example_input = st.text_input("Enter your prompt text:", value=prompt, help="Enter text to get a response.")
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#example_input = st.text_area("Enter Prompt :", '', height=100
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# Search History to ArXiv
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session_state = {}
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if "search_queries" not in session_state:
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session_state["search_queries"] = []
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# Search AI
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query=example_input
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if query:
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search_arxiv(query)
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search_glossary(query)
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st.markdown(' ')
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st.write("Search history:")
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for example_input in session_state["search_queries"]:
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