On this tutorial, we’ll assemble a strong and interactive Streamlit utility that brings collectively the capabilities of LangChain, the Google Gemini API, and a group of superior devices to create a clever AI assistant. Using Streamlit’s intuitive interface, we’ll create a chat-based system which will search the online, fetch Wikipedia content material materials, perform calculations, keep in mind key particulars, and cope with dialog historic previous, all in precise time. Whether or not or not we’re builders, researchers, or just exploring AI, this setup permits us to work along with a multi-agent system instantly from the browser with minimal code and most flexibility.
!pip arrange -q streamlit langchain langchain-google-genai langchain-community
!pip arrange -q pyngrok python-dotenv wikipedia duckduckgo-search
!npm arrange -g localtunnel
import streamlit as st
import os
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.brokers import create_react_agent, AgentExecutor
from langchain.devices import Software program, WikipediaQueryRun, DuckDuckGoSearchRun
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain.callbacks.streamlit import StreamlitCallbackHandler
from langchain_community.utilities import WikipediaAPIWrapper, DuckDuckGoSearchAPIWrapper
import asyncio
import threading
import time
from datetime import datetime
import jsonWe begin by placing in all of the obligatory Python and Node.js packages required for our AI assistant app. This accommodates Streamlit for the frontend, LangChain for agent logic, and devices like Wikipedia, DuckDuckGo, and ngrok/localtunnel for exterior search and web internet hosting. As quickly as organize, we import all modules to begin out establishing our interactive multi-tool AI agent.
GOOGLE_API_KEY = "Use Your API Key Proper right here"
NGROK_AUTH_TOKEN = "Use Your Auth Token Proper right here"
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEYSubsequent, we configure the setting by setting the Google Gemini API key and the ngrok authentication token. We assign these credentials to variables and set the GOOGLE_API_KEY so the LangChain agent can securely entry the Gemini model all through execution.
class InnovativeAgentTools:
"""Superior instrument assortment for the multi-agent system"""
@staticmethod
def get_calculator_tool():
def calculate(expression: str) -> str:
"""Calculate mathematical expressions safely"""
try:
allowed_chars = set('0123456789+-*/.() ')
if all(c in allowed_chars for c in expression):
finish outcome = eval(expression)
return f"Consequence: {finish outcome}"
else:
return "Error: Invalid mathematical expression"
in addition to Exception as e:
return f"Calculation error: {str(e)}"
return Software program(
determine="Calculator",
func=calculate,
description="Calculate mathematical expressions. Enter must be a sound math expression."
)
@staticmethod
def get_memory_tool(memory_store):
def save_memory(key_value: str) -> str:
"""Save knowledge to memory"""
try:
key, value = key_value.break up(":", 1)
memory_store[key.strip()] = value.strip()
return f"Saved '{key.strip()}' to memory"
in addition to:
return "Error: Use format 'key: value'"
def recall_memory(key: str) -> str:
"""Recall knowledge from memory"""
return memory_store.get(key.strip(), f"No memory found for '{key}'")
return [
Tool(name="SaveMemory", func=save_memory,
description="Save information to memory. Format: 'key: value'"),
Tool(name="RecallMemory", func=recall_memory,
description="Recall saved information. Input: key to recall")
]
@staticmethod
def get_datetime_tool():
def get_current_datetime(format_type: str = "full") -> str:
"""Get current date and time"""
now = datetime.now()
if format_type == "date":
return now.strftime("%Y-%m-%d")
elif format_type == "time":
return now.strftime("%H:%M:%S")
else:
return now.strftime("%Y-%m-%d %H:%M:%S")
return Software program(
determine="DateTime",
func=get_current_datetime,
description="Get current date/time. Decisions: 'date', 'time', or 'full'"
)Proper right here, we define the InnovativeAgentTools class to equip our AI agent with specialised capabilities. We implement devices equal to a Calculator for safe expression evaluation, Memory Devices to avoid wasting a lot of and recall knowledge all through turns, and a date and time instrument to fetch the current date and time. These devices enable our Streamlit AI agent to motive, keep in mind, and reply contextually, very like an actual assistant. Strive the full Pocket guide proper right here
class MultiAgentSystem:
"""Progressive multi-agent system with specialised capabilities"""
def __init__(self, api_key: str):
self.llm = ChatGoogleGenerativeAI(
model="gemini-pro",
google_api_key=api_key,
temperature=0.7,
convert_system_message_to_human=True
)
self.memory_store = {}
self.conversation_memory = ConversationBufferWindowMemory(
memory_key="chat_history",
okay=10,
return_messages=True
)
self.devices = self._initialize_tools()
self.agent = self._create_agent()
def _initialize_tools(self):
"""Initialize all on the market devices"""
devices = []
devices.lengthen([
DuckDuckGoSearchRun(api_wrapper=DuckDuckGoSearchAPIWrapper()),
WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
])
devices.append(InnovativeAgentTools.get_calculator_tool())
devices.append(InnovativeAgentTools.get_datetime_tool())
devices.lengthen(InnovativeAgentTools.get_memory_tool(self.memory_store))
return devices
def _create_agent(self):
"""Create the ReAct agent with superior speedy"""
speedy = PromptTemplate.from_template("""
🤖 You is perhaps a sophisticated AI assistant with entry to plenty of devices and continual memory.
AVAILABLE TOOLS:
{devices}
TOOL USAGE FORMAT:
- Suppose step-by-step about what it is important to do
- Use Movement: tool_name
- Use Movement Enter: your enter
- Anticipate Comment
- Proceed until you might need a closing reply
MEMORY CAPABILITIES:
- It could possibly prevent important knowledge using SaveMemory
- You'll recall earlier knowledge using RecallMemory
- Always try to remember client preferences and context
CONVERSATION HISTORY:
{chat_history}
CURRENT QUESTION: {enter}
REASONING PROCESS:
{agent_scratchpad}
Begin your response alongside along with your thought course of, then take movement if wished.
""")
agent = create_react_agent(self.llm, self.devices, speedy)
return AgentExecutor(
agent=agent,
devices=self.devices,
memory=self.conversation_memory,
verbose=True,
handle_parsing_errors=True,
max_iterations=5
)
def chat(self, message: str, callback_handler=None):
"""Course of client message and return response"""
try:
if callback_handler:
response = self.agent.invoke(
{"enter": message},
{"callbacks": [callback_handler]}
)
else:
response = self.agent.invoke({"enter": message})
return response["output"]
in addition to Exception as e:
return f"Error processing request: {str(e)}"On this half, we assemble the core of our utility, the MultiAgentSystem class. Proper right here, we mix the Gemini Skilled model using LangChain and initialize all vital devices, along with web search, memory, and calculator capabilities. We configure a ReAct-style agent using a custom-made speedy that guides instrument utilization and memory coping with. Lastly, we define a chat method that permits the agent to course of client enter, invoke devices when wanted, and generate intelligent, context-aware responses. Strive the full Pocket guide proper right here
def create_streamlit_app():
"""Create the progressive Streamlit utility"""
st.set_page_config(
page_title="🚀 Superior LangChain Agent with Gemini",
page_icon="🤖",
construction="intensive",
initial_sidebar_state="expanded"
)
st.markdown("""
""", unsafe_allow_html=True)
st.markdown("""
Powered by LangChain + Gemini API + Streamlit
""", unsafe_allow_html=True)
with st.sidebar:
st.header("🔧 Configuration")
api_key = st.text_input(
"🔑 Google AI API Key",
sort="password",
value=GOOGLE_API_KEY if GOOGLE_API_KEY != "your-gemini-api-key-here" else "",
help="Get your API key from https://ai.google.dev/"
)
if not api_key:
st.error("Please enter your Google AI API key to proceed")
st.stop()
st.success("✅ API Key configured")
st.header("🤖 Agent Capabilities")
st.markdown("""
- 🔍 **Internet Search** (DuckDuckGo)
- 📚 **Wikipedia Lookup**
- 🧮 **Mathematical Calculator**
- 🧠 **Persistent Memory**
- 📅 **Date & Time**
- 💬 **Dialog Historic previous**
""")
if 'agent_system' in st.session_state:
st.header("🧠 Memory Retailer")
memory = st.session_state.agent_system.memory_store
if memory:
for key, value in memory.devices():
st.markdown(f"""
{key}: {value}
""", unsafe_allow_html=True)
else:
st.knowledge("No reminiscences saved however")
if 'agent_system' not in st.session_state:
with st.spinner("🔄 Initializing Superior Agent System..."):
st.session_state.agent_system = MultiAgentSystem(api_key)
st.success("✅ Agent System Ready!")
st.header("💬 Interactive Chat")
if 'messages' not in st.session_state:
st.session_state.messages = [{
"role": "assistant",
"content": """🤖 Hello! I'm your advanced AI assistant powered by Gemini. I can:
• Search the web and Wikipedia for information
• Perform mathematical calculations
• Remember important information across our conversation
• Provide current date and time
• Maintain conversation context
Try asking me something like:
- "Calculate 15 * 8 + 32"
- "Search for recent news about AI"
- "Remember that my favorite color is blue"
- "What's the current time?"
"""
}]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if speedy := st.chat_input("Ask me one thing..."):
st.session_state.messages.append({"place": "client", "content material materials": speedy})
with st.chat_message("client"):
st.markdown(speedy)
with st.chat_message("assistant"):
callback_handler = StreamlitCallbackHandler(st.container())
with st.spinner("🤔 Pondering..."):
response = st.session_state.agent_system.chat(speedy, callback_handler)
st.markdown(f"""
{response}
""", unsafe_allow_html=True)
st.session_state.messages.append({"place": "assistant", "content material materials": response})
st.header("💡 Occasion Queries")
col1, col2, col3 = st.columns(3)
with col1:
if st.button("🔍 Search Occasion"):
occasion = "Look for the latest developments in quantum computing"
st.session_state.example_query = occasion
with col2:
if st.button("🧮 Math Occasion"):
occasion = "Calculate the compound curiosity on $1000 at 5% for 3 years"
st.session_state.example_query = occasion
with col3:
if st.button("🧠 Memory Occasion"):
occasion = "Don't forget that I work as an info scientist at TechCorp"
st.session_state.example_query = occasion
if 'example_query' in st.session_state:
st.knowledge(f"Occasion query: {st.session_state.example_query}")On this half, we convey the whole thing collectively by establishing an interactive web interface using Streamlit. We configure the app construction, define custom-made CSS varieties, and organize a sidebar for inputting API keys and configuring agent capabilities. We initialize the multi-agent system, protect a message historic previous, and permit a chat interface that permits clients to work collectively in real-time. To make it even less complicated to find, we moreover current occasion buttons for search, math, and memory-related queries, all in a fantastically styled, responsive UI. Strive the full Pocket guide proper right here
def setup_ngrok_auth(auth_token):
"""Setup ngrok authentication"""
try:
from pyngrok import ngrok, conf
conf.get_default().auth_token = auth_token
try:
tunnels = ngrok.get_tunnels()
print("✅ Ngrok authentication worthwhile!")
return True
in addition to Exception as e:
print(f"❌ Ngrok authentication failed: {e}")
return False
in addition to ImportError:
print("❌ pyngrok not put in. Placing in...")
import subprocess
subprocess.run(['pip', 'install', 'pyngrok'], study=True)
return setup_ngrok_auth(auth_token)
def get_ngrok_token_instructions():
"""Current instructions for getting ngrok token"""
return """
🔧 NGROK AUTHENTICATION SETUP:
1. Be part of an ngrok account:
- Go to: https://dashboard.ngrok.com/signup
- Create a free account
2. Get your authentication token:
- Go to: https://dashboard.ngrok.com/get-started/your-authtoken
- Copy your authtoken
3. Substitute 'your-ngrok-auth-token-here' throughout the code alongside along with your exact token
4. Totally different methods if ngrok fails:
- Use Google Colab's built-in public URL attribute
- Use localtunnel: !npx localtunnel --port 8501
- Use serveo.web: !ssh -R 80:localhost:8501 serveo.web
"""
Proper right here, we organize a helper carry out to authenticate ngrok, which allows us to indicate our native Streamlit app to the online. We use the pyngrok library to configure the authentication token and ensure the connection. If the token is missing or invalid, we provide detailed instructions on the suitable solution to obtain one and suggest varied tunneling methods, equal to LocalTunnel or Serveo, making it easy for us to host and share our app from environments like Google Colab.
def important():
"""Elementary carry out to run the equipment"""
try:
create_streamlit_app()
in addition to Exception as e:
st.error(f"Utility error: {str(e)}")
st.knowledge("Please study your API key and take a look at refreshing the online web page")This important() carry out acts as a result of the entry degree for our Streamlit utility. We merely title create_streamlit_app() to launch the whole interface. If one thing goes fallacious, equal to a missing API key or a failed instrument initialization, we catch the error gracefully and present a helpful message, guaranteeing the patron is conscious of the suitable solution to get nicely and proceed using the app simply.
def run_in_colab():
"""Run the equipment in Google Colab with right ngrok setup"""
print("🚀 Starting Superior LangChain Agent Setup...")
if NGROK_AUTH_TOKEN == "your-ngrok-auth-token-here":
print("⚠️ NGROK_AUTH_TOKEN not configured!")
print(get_ngrok_token_instructions())
print("🔄 Attempting varied tunnel methods...")
try_alternative_tunnels()
return
print("📦 Placing in required packages...")
import subprocess
packages = [
'streamlit',
'langchain',
'langchain-google-genai',
'langchain-community',
'wikipedia',
'duckduckgo-search',
'pyngrok'
]
for bundle in packages:
try:
subprocess.run(['pip', 'install', package], study=True, capture_output=True)
print(f"✅ {bundle} put in")
in addition to subprocess.CalledProcessError:
print(f"⚠️ Didn't put in {bundle}")
app_content=""'
import streamlit as st
import os
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.brokers import create_react_agent, AgentExecutor
from langchain.devices import Software program, WikipediaQueryRun, DuckDuckGoSearchRun
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain.callbacks.streamlit import StreamlitCallbackHandler
from langchain_community.utilities import WikipediaAPIWrapper, DuckDuckGoSearchAPIWrapper
from datetime import datetime
# Configuration - Substitute alongside along with your exact keys
GOOGLE_API_KEY = "''' + GOOGLE_API_KEY + '''"
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
class InnovativeAgentTools:
@staticmethod
def get_calculator_tool():
def calculate(expression: str) -> str:
try:
allowed_chars = set('0123456789+-*/.() ')
if all(c in allowed_chars for c in expression):
finish outcome = eval(expression)
return f"Consequence: {finish outcome}"
else:
return "Error: Invalid mathematical expression"
in addition to Exception as e:
return f"Calculation error: {str(e)}"
return Software program(determine="Calculator", func=calculate,
description="Calculate mathematical expressions. Enter must be a sound math expression.")
@staticmethod
def get_memory_tool(memory_store):
def save_memory(key_value: str) -> str:
try:
key, value = key_value.break up(":", 1)
memory_store[key.strip()] = value.strip()
return f"Saved '{key.strip()}' to memory"
in addition to:
return "Error: Use format 'key: value'"
def recall_memory(key: str) -> str:
return memory_store.get(key.strip(), f"No memory found for '{key}'")
return [
Tool(name="SaveMemory", func=save_memory, description="Save information to memory. Format: 'key: value'"),
Tool(name="RecallMemory", func=recall_memory, description="Recall saved information. Input: key to recall")
]
@staticmethod
def get_datetime_tool():
def get_current_datetime(format_type: str = "full") -> str:
now = datetime.now()
if format_type == "date":
return now.strftime("%Y-%m-%d")
elif format_type == "time":
return now.strftime("%H:%M:%S")
else:
return now.strftime("%Y-%m-%d %H:%M:%S")
return Software program(determine="DateTime", func=get_current_datetime,
description="Get current date/time. Decisions: 'date', 'time', or 'full'")
class MultiAgentSystem:
def __init__(self, api_key: str):
self.llm = ChatGoogleGenerativeAI(
model="gemini-pro",
google_api_key=api_key,
temperature=0.7,
convert_system_message_to_human=True
)
self.memory_store = {}
self.conversation_memory = ConversationBufferWindowMemory(
memory_key="chat_history", okay=10, return_messages=True
)
self.devices = self._initialize_tools()
self.agent = self._create_agent()
def _initialize_tools(self):
devices = []
try:
devices.lengthen([
DuckDuckGoSearchRun(api_wrapper=DuckDuckGoSearchAPIWrapper()),
WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
])
in addition to Exception as e:
st.warning(f"Search devices might need restricted efficiency: {e}")
devices.append(InnovativeAgentTools.get_calculator_tool())
devices.append(InnovativeAgentTools.get_datetime_tool())
devices.lengthen(InnovativeAgentTools.get_memory_tool(self.memory_store))
return devices
def _create_agent(self):
speedy = PromptTemplate.from_template("""
🤖 You is perhaps a sophisticated AI assistant with entry to plenty of devices and continual memory.
AVAILABLE TOOLS:
{devices}
TOOL USAGE FORMAT:
- Suppose step-by-step about what it is important to do
- Use Movement: tool_name
- Use Movement Enter: your enter
- Anticipate Comment
- Proceed until you might need a closing reply
CONVERSATION HISTORY:
{chat_history}
CURRENT QUESTION: {enter}
REASONING PROCESS:
{agent_scratchpad}
Begin your response alongside along with your thought course of, then take movement if wished.
""")
agent = create_react_agent(self.llm, self.devices, speedy)
return AgentExecutor(agent=agent, devices=self.devices, memory=self.conversation_memory,
verbose=True, handle_parsing_errors=True, max_iterations=5)
def chat(self, message: str, callback_handler=None):
try:
if callback_handler:
response = self.agent.invoke({"enter": message}, {"callbacks": [callback_handler]})
else:
response = self.agent.invoke({"enter": message})
return response["output"]
in addition to Exception as e:
return f"Error processing request: {str(e)}"
# Streamlit App
st.set_page_config(page_title="🚀 Superior LangChain Agent", page_icon="🤖", construction="intensive")
st.markdown("""
""", unsafe_allow_html=True)
st.markdown('Powered by LangChain + Gemini API
', unsafe_allow_html=True)
with st.sidebar:
st.header("🔧 Configuration")
api_key = st.text_input("🔑 Google AI API Key", sort="password", value=GOOGLE_API_KEY)
if not api_key:
st.error("Please enter your Google AI API key")
st.stop()
st.success("✅ API Key configured")
st.header("🤖 Agent Capabilities")
st.markdown("- 🔍 Internet Searchn- 📚 Wikipedian- 🧮 Calculatorn- 🧠 Reminiscencen- 📅 Date/Time")
if 'agent_system' in st.session_state and st.session_state.agent_system.memory_store:
st.header("🧠 Memory Retailer")
for key, value in st.session_state.agent_system.memory_store.devices():
st.markdown(f'{key}: {value}
', unsafe_allow_html=True)
if 'agent_system' not in st.session_state:
with st.spinner("🔄 Initializing Agent..."):
st.session_state.agent_system = MultiAgentSystem(api_key)
st.success("✅ Agent Ready!")
if 'messages' not in st.session_state:
st.session_state.messages = [{
"role": "assistant",
"content": "🤖 Hello! I'm your advanced AI assistant. I can search, calculate, remember information, and more! Try asking me to: calculate something, search for information, or remember a fact about you."
}]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if speedy := st.chat_input("Ask me one thing..."):
st.session_state.messages.append({"place": "client", "content material materials": speedy})
with st.chat_message("client"):
st.markdown(speedy)
with st.chat_message("assistant"):
callback_handler = StreamlitCallbackHandler(st.container())
with st.spinner("🤔 Pondering..."):
response = st.session_state.agent_system.chat(speedy, callback_handler)
st.markdown(f'{response}
', unsafe_allow_html=True)
st.session_state.messages.append({"place": "assistant", "content material materials": response})
# Occasion buttons
st.header("💡 Try These Examples")
col1, col2, col3 = st.columns(3)
with col1:
if st.button("🧮 Calculate 15 * 8 + 32"):
st.rerun()
with col2:
if st.button("🔍 Search AI info"):
st.rerun()
with col3:
if st.button("🧠 Keep in mind my determine is Alex"):
st.rerun()
'''
with open('streamlit_app.py', 'w') as f:
f.write(app_content)
print("✅ Streamlit app file created effectively!")
if setup_ngrok_auth(NGROK_AUTH_TOKEN):
start_streamlit_with_ngrok()
else:
print("❌ Ngrok authentication failed. Attempting varied methods...")
try_alternative_tunnels()Inside the run_in_colab() carry out, we make it easy to deploy the Streamlit app instantly from a Google Colab ambiance. We begin by placing in all required packages, then dynamically generate and write the entire Streamlit app code to a streamlit_app.py file. We affirm the presence of a sound ngrok token to permit public entry to the app from Colab, and if it’s missing or invalid, we info ourselves via fallback tunneling selections. This setup permits us to work along with our AI agent from anyplace, all inside just some cells in Colab. Strive the full Pocket guide proper right here
def start_streamlit_with_ngrok():
"""Start Streamlit with ngrok tunnel"""
import subprocess
import threading
from pyngrok import ngrok
def start_streamlit():
subprocess.run(['streamlit', 'run', 'streamlit_app.py', '--server.port=8501', '--server.headless=true'])
print("🚀 Starting Streamlit server...")
thread = threading.Thread(purpose=start_streamlit)
thread.daemon = True
thread.start()
time.sleep(5)
try:
print("🌐 Creating ngrok tunnel...")
public_url = ngrok.be part of(8501)
print(f"🔗 SUCCESS! Entry your app at: {public_url}")
print("✨ Your Superior LangChain Agent is now working publicly!")
print("📱 You'll share this URL with others!")
print("⏳ Holding tunnel alive... Press Ctrl+C to stop")
try:
ngrok_process = ngrok.get_ngrok_process()
ngrok_process.proc.wait()
in addition to KeyboardInterrupt:
print("👋 Shutting down...")
ngrok.kill()
in addition to Exception as e:
print(f"❌ Ngrok tunnel failed: {e}")
try_alternative_tunnels()
def try_alternative_tunnels():
"""Try varied tunneling methods"""
print("🔄 Attempting varied tunnel methods...")
import subprocess
import threading
def start_streamlit():
subprocess.run(['streamlit', 'run', 'streamlit_app.py', '--server.port=8501', '--server.headless=true'])
thread = threading.Thread(purpose=start_streamlit)
thread.daemon = True
thread.start()
time.sleep(3)
print("🌐 Streamlit is engaged on http://localhost:8501")
print("n📋 ALTERNATIVE TUNNEL OPTIONS:")
print("1. localtunnel: Run this in a model new cell:")
print(" !npx localtunnel --port 8501")
print("n2. serveo.web: Run this in a model new cell:")
print(" !ssh -R 80:localhost:8501 serveo.web")
print("n3. Colab public URL (if on the market):")
print(" Use the 'Public URL' button in Colab's interface")
try:
whereas True:
time.sleep(60)
in addition to KeyboardInterrupt:
print("👋 Shutting down...")
if __name__ == "__main__":
try:
get_ipython()
print("🚀 Google Colab detected - starting setup...")
run_in_colab()
in addition to NameError:
important()On this closing half, we organize the execution logic to run the app each in an space ambiance or inside Google Colab. The start_streamlit_with_ngrok() carry out launches the Streamlit server throughout the background and makes use of ngrok to indicate it publicly, making it easy to entry and share. If ngrok fails, the try_alternative_tunnels() carry out prompts with varied tunneling selections, equal to LocalTunnel and Serveo. With the __main__ block, we routinely detect if we’re in Colab and launch the appropriate setup, making the whole deployment course of straightforward, versatile, and shareable from anyplace.
In conclusion, we’ll have a very helpful AI agent working inside a contemporary Streamlit interface, in a position to answering queries, remembering client inputs, and even sharing its firms publicly using ngrok. We’ve seen how merely Streamlit permits us to mix superior AI functionalities into an attractive and user-friendly app. From proper right here, we are going to broaden the agent’s devices, plug it into greater workflows, or deploy it as part of our intelligent features. With Streamlit as a result of the front-end and LangChain brokers powering the logic, we’ve constructed a secure foundation for next-gen interactive AI experiences.
Strive the full Pocket guide proper right here. All credit score rating for this evaluation goes to the researchers of this endeavor. Moreover, be comfortable to adjust to us on Twitter and don’t overlook to hitch our 100k+ ML SubReddit and Subscribe to our E-newsletter.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is devoted to harnessing the potential of Artificial Intelligence for social good. His most modern endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth safety of machine learning and deep learning info that’s every technically sound and easily understandable by a big viewers. The platform boasts of over 2 million month-to-month views, illustrating its recognition amongst audiences.
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