Streamlit jobs
...datasets) that truly represents urban crash scenarios, then fine-tune an architecture such as YOLOv8, Faster R-CNN, or another proven model in PyTorch/TensorFlow. Accuracy and speed matter equally; anything under a few seconds for a one-minute clip on a modern GPU is ideal. Deliverables • Trained model files and all preprocessing scripts • Lightweight desktop or web demo (Python + OpenCV/Streamlit/Flask—your call) mirroring the UI flow above • At least three short demo videos that clearly show LOW, MEDIUM, and HIGH severity outputs • Brief setup guide so I can reproduce results locally or on a cloud VM This is time-sensitive, so please outline how quickly you can: 1. Finalize the dataset, 2. Train and validate the model, 3. Package the ...
...financial data APIs or market data sources. - Previous work on financial analytics dashboards or research tools. - Familiarity with Google Cloud Platform services (Vertex AI, Looker, Dataflow, etc.). Deliverables: - Well-structured BigQuery database with clean data schema. - Integrated Machine Learning models for bond price research features. - Functional search and analysis interface (e.g., Streamlit, Looker, or simple web app). - Documentation and basic user guide. - 1–2 weeks of support after delivery. How to Apply: Please include in your proposal: - Your experience with BigQuery and Machine Learning projects. - Any relevant work with financial or time-series data (even if not bonds). - A short description of how you would approach this project. - Estimated timel...
...connect fresh financial files • Inspect the cleaned dataset and calculated ratios • View the readiness score with drill-down explanations • Toggle assumptions, refresh predictions in real time and export a concise report Deliverables: 1. End-to-end Python (or comparable) codebase covering automated ETL, feature engineering, model training and inference. 2. A lightweight web dashboard—Streamlit, Dash or similar—deployed locally or on a small cloud instance. 3. Brief technical documentation and a walkthrough so new users can reproduce results and retrain the model with updated data. Acceptance will be based on: • Successful automation of data cleaning for a sample of multi-year hospital statements. • Generation of ratios ma...
...the probability of a price rise in the next 10 seconds (not the full 5 minutes). Role: Full-stack ML engineer → data pipeline + LSTM/XGBoost model + backend API + frontend dashboard. Tasks: Ingest BTC spot price (1s or 1min), VIX, Polymarket token price, order book data Train LSTM or XGBoost to output probability of price increase in next 10 seconds Build FastAPI backend + simple frontend (Streamlit/React) Integrate with Polymarket CLOB API for live & paper trading Required skills: LSTM or XGBoost for ultra-short time series classification Built crypto trading bots before (provide links) Python, real-time WebSocket pipelines Polymarket API or Gnosis Conditional Tokens experience In your proposal: 2 past relevant projects Model choice (LSTM/XGBoost) and why ...
...data and produce validated insights, not just build a working pipeline. Scope of Work The project includes: • Data processing of large job datasets (10k–50k+ rows) • Extracting technical skills (Python, SQL, AWS, etc.) from job descriptions • Salary analysis and estimation (validated using official BLS data) • Job demand trend analysis and forecasting • Building an interactive dashboard (Streamlit preferred) • Designing and executing research experiments with clear evaluation metrics Research Requirements (Core of Project) You must treat this as a research problem, not just implementation. 1. Skill Extraction Reliability • Extract skills from job descriptions using multiple approaches, such as: • Rule-based (regex / keywo...
...presets or data import The tool is scientific in nature and requires clean, modern scientific visualization (using libraries such as Plotly, Matplotlib, or D3.js). The final product should feel like a polished research or policy-analysis dashboard — intuitive for both technical and non-technical users. Key Requirements Modern, responsive web-based GUI (preferably Python-based backend with Dash/Streamlit or equivalent React + FastAPI stack) Real-time or near real-time simulation and visualization Clear display of equilibrium states and system topology Ability to save, export, and share graphical results Secure user/session management Excellent code quality, documentation, and maintainability Confidentiality This is a highly confidential project. You will be required to si...
...selection and tuning. Evaluation must be demonstrated through both a train/test split and k-fold cross-validation, with clear error metrics that suit the problem type (classification or regression). I will rely on your plots—ROC curves, learning curves, residuals, and any other insightful graphs—as well as correlation heatmaps to explain feature relationships. An interactive dashboard (Plotly Dash, Streamlit, or a comparable Python framework) is expected so stakeholders can explore results themselves. Deliverables • Fully commented Python code or notebooks, ready to run end-to-end • Reproducible environment details ( or ) • Technical report (PDF or Markdown) describing data prep, model logic, parameter choices, evaluation results, and key insights ...
...presets or data import The tool is scientific in nature and requires clean, modern scientific visualization (using libraries such as Plotly, Matplotlib, or D3.js). The final product should feel like a polished research or policy-analysis dashboard — intuitive for both technical and non-technical users. Key Requirements Modern, responsive web-based GUI (preferably Python-based backend with Dash/Streamlit or equivalent React + FastAPI stack) Real-time or near real-time simulation and visualization Clear display of equilibrium states and system topology Ability to save, export, and share graphical results Secure user/session management Excellent code quality, documentation, and maintainability Confidentiality This is a highly confidential project. You will be required to si...
...interactive dashboard (Streamlit preferred) • Supporting research experiments and evaluation Research Requirements (Very Important): You must work on all 3: 1. Skill Extraction Reliability • Extract skills from job descriptions • Compare multiple methods • Evaluate (precision, recall, F1) 2. Salary Estimation • Predict salary from job + skills • Validate using BLS data • Analyze uncertainty 3. Job Demand Forecasting • Analyze trends over time • Build forecasting model • Evaluate reliability Dataset Requirement: You may use any dataset, but it must: • include job descriptions • include modern skills (Python, SQL, AWS, etc.) • be large (10k+ rows minimum) Deliverables: • Clean dataset • Skill...
...position sizing per stock Key Features Earnings filter (blocks trades + auto-closes before earnings) Fully automated execution Real-time tracking + manual override Dashboard Manage 100 stocks (timeframe + capital per stock) Live positions, P&L, trade logs Status: Long / Short / Waiting / Blocked Bulk edit + secure login Tech Python (FastAPI), QuantConnect, IBKR integration Frontend: React / Streamlit (open to suggestions) Ready TradingView indicator (built) IBKR account (live) VPS + QuantConnect account Deliverables Fully deployed system (live-ready) Dashboard + multi-timeframe logic Earnings filter working Setup guide + handover...
... Why This Project Stands Out: This is not just a basic app – it’s a smart fitness assistant that helps users make better health decisions with ease. * Features You’ll Love: Full Body, Leg & Fat Loss Workout Plans Pre & Post Workout Meal Suggestions Fast, Responsive & Clean User Interface AI-Based Smart Recommendations Beginner-Friendly & Easy to Use Built With Modern Tech: Python | Streamlit | AI Integration Perfect For: • Fitness Coaches & Trainers • Health & Wellness Startups • Students & Portfolio Projects • Anyone building a fitness-related platform What You Get: * Complete Working Web Application * Source Code (Clean & Structured) *Easy Customization Options * Support for Enhancements Turn ...
...Once a face is found, a TensorFlow model should assign an emotion label (happy, sad, angry, surprised, neutral, etc.) together with its confidence score. The video stream has to overlay these results live, log every reading with a timestamp, and trigger a visual or audible alert whenever negative emotions are detected repeatedly within a short window. A lightweight dashboard served with either Streamlit or Flask will let me: • watch the annotated video feed • view rolling emotion statistics and charts • review and download the timestamped log of events and alerts Optimisation for Jetson (CUDA, cuDNN, TensorRT where appropriate) is essential, and the finished app should launch from a single command, open the dashboard in a browser, sustain real-time perform...
...database) with a timestamp, the detected emotion, and its confidence. When the same negative emotion (sad, angry, fearful, disgust) is observed repeatedly over a configurable window, an alert must fire—initially an on-screen banner and a console message are enough, but leave hooks so I can later plug in email or MQTT notifications. For visibility, wrap the system in a small dashboard built with Streamlit or Flask. The dashboard should: • stream the annotated video, • show the latest emotion and confidence, • display a rolling chart or table of recent detections, and • expose simple sliders or text fields to adjust alert thresholds without restarting the app. Acceptance criteria • Runs on Jetson Xavier NX / Nano with a USB webcam at ≥15...
...NVIDIA Jetson environment • Connect and configure camera (USB/IP) • Implement real-time face detection • Integrate pre-trained emotion recognition model (no training required) • Display emotion + confidence score live • Log events with timestamps • Build a simple dashboard (Streamlit or Flask) • Add alert logic (e.g. repeated negative emotion → warning) ⸻ Required Skills • NVIDIA Jetson (VERY IMPORTANT) • Python • OpenCV • TensorFlow or PyTorch • Real-time video processing • Linux / Ubuntu • Flask or Streamlit Bonus: • GStreamer / DeepStream experience • Previous edge AI or surveillance projects ⸻ Deliverables • Fully working prototype on Jetson • Clean sou...
I have an AI-focused Python concept that I now want packaged into a clean Streamlit web app. The core idea is to run one or more autonomous “agents” that process data and return insights in real time; from there, the user should be able to trigger classification, regression, or clustering logic (whichever best fits the uploaded dataset) and immediately view results inside the same interface. Here’s what I need from you: • A lightweight, well-structured Python codebase that spins up a Streamlit app, loads the agent logic, and exposes controls for the three model modes mentioned above. • Clear separation between data ingest, model inference, and UI layers so I can swap in new models later. • Simple, attractive visualisations for prediction...
...NVIDIA Jetson environment • Connect and configure camera (USB/IP) • Implement real-time face detection • Integrate pre-trained emotion recognition model (no training required) • Display emotion + confidence score live • Log events with timestamps • Build a simple dashboard (Streamlit or Flask) • Add alert logic (e.g. repeated negative emotion → warning) ⸻ Required Skills • NVIDIA Jetson (VERY IMPORTANT) • Python • OpenCV • TensorFlow or PyTorch • Real-time video processing • Linux / Ubuntu • Flask or Streamlit Bonus: • GStreamer / DeepStream experience • Previous edge AI or surveillance projects ⸻ Deliverables • Fully working prototype on Jetson • Clean sou...
...status URL column name original URL final URL status status code error post column name post text word count flag reason User interface Build a simple web app with: workbook upload reporting mode selector complete workbook option week selector start date and end date selector run validation button progress display summary preview download buttons for Excel, Word, and PDF Preferred stack Python Streamlit or Flask pandas openpyxl requests BeautifulSoup python-docx ReportLab, WeasyPrint, or HTML to PDF Functional details Workbook handling support multiple worksheets ignore empty sheets safely handle inconsistent headers where possible log worksheet level issues without stopping the run Date and week handling parse mixed date formats safely support Excel date cells and text da...
...JSON Generation Output MUST follow the new Divi 5 "Gutenberg-style" syntax. Requirements: Use `` comments with unique _id values and correct JSON properties inside the post_content string. No Raw HTML: Mapping must use native Divi 5 module settings (Colors, Padding, Fonts, Flexbox) to ensure the layout is fully editable. Technical Stack Language: Python (FastAPI/Flask for backend, PyQt or Streamlit for local UI). Browser Automation: Playwright or Selenium (Stealth mode). OS: Windows 11. Optimization: Must be able to handle local inference calls via RTX 5090 (CUDA). Budget & Milestones ($1000 Total) Milestone 1 ($200): Functional Site Crawler (URL Listing & Selection). Milestone 2 ($400): Core Conversion Engine (Successfully importing a complex Section i...
Scope of delivery: Streamlit Web App: A login page for StoxKart credentials. A main page showing current market prices with buy-sell options. A second page showing open positions, margins, and basic account info. Integration Notes: The app will display prices and provide buy-sell buttons. Any further configuration, Excel logic, or additional settings will be handled by you. Modules: Step 1 (login & market prices + buy-sell options). Step 2 (open positions, margin page, display).
...Sentiment Analysis: Detect and report user sentiment during interactions. - Dialect Identification: Identify the Arabic dialect used by the user (e.g., MSA vs dialectal Arabic). - Text Summarization: Generate concise summaries for longer Arabic texts. Interface Requirements The assistant should be interactive and implemented using one of the following interfaces: - Command-line interface (CLI) - Streamlit application - Chatbot API integration Use of Tools You are free to choose tools, libraries, datasets, and models. However: At least one traditional Arabic NLP component must be clearly implemented and justified LLM usage must be documented (models, prompts, assumptions, limitations) Deliverables 1) Source Code All source code used to implement the system, clearly organized a...
...experience to build an academic project related to healthcare data validation (LIS domain). Project Scope: Input: CSV dataset (synthetic healthcare/lab data) Perform rule-based validation: • Range checks • Missing values • Format validation Implement simple ML anomaly detection (Isolation Forest preferred) Generate validation report (valid/invalid + errors) Optional: simple dashboard (Streamlit) Deliverables: Python code (well commented) Jupyter notebook or script Mini + major project report IEEE format paper PPT Basic explanation of project Requirements: Python (Pandas, Scikit-learn) Basic ML knowledge Should be able to explain the implementation Timeline: 10–15 days Budget: ₹15,000 - 20,000 Important: No copy-paste or ready-made projects Work ...
Implement a complete deep learning workflow capable of identifying plant diseases from raw leaf images and presenting the predictions through an interactive Streamlit-based web application. Since no dataset is currently available, the first step will involve searching and selecting a suitable plant disease dataset from Kaggle. Multiple datasets may be reviewed and compared based on criteria such as dataset size, number of disease categories, labeling quality, and class balance. The final dataset should contain a sufficient number of images per class to support reliable model training and evaluation. After obtaining the dataset, the next phase will focus on data understanding and preparation using a Jupyter Notebook. This will include exploratory data analysis (EDA) to study the di...
We are looking for an experienced Python developer to build a Streamlit web application
...Requirements: - Engaging and interactive presentation of AI capabilities - High-quality visuals and graphics - Clear and concise messaging tailored to potential clients - Experience in marketing materials and AI technologies is a plus Scope • Build the complete workflow in Python, using current mainstream libraries (scikit-learn, TensorFlow or PyTorch, plus supporting tools such as pandas, NumPy, and Streamlit/Plotly for visual insights). • Provide clean, reproducible code, modular enough to be adapted later. • Document the entire pipeline with architecture and data-flow diagrams, plus a concise narrative that explains each stage to a non-technical audience. • Incorporate rigorous evaluation: baseline, key metrics, and a brief ablation or feature-importance a...
Title: AI Developer Needed – Simple Art Image Analysis Web App (Prototype) Project Descripti...and simplicity. Core Features: - Upload artwork image - Image analysis using an AI model - Detect artistic style or possible artist - Generate a short explanation about the artwork Examples of artworks: - The Starry Night – Vincent van Gogh - Mona Lisa – Leonardo da Vinci Preferred Technologies (flexible): - Python - Computer vision or existing AI APIs - Simple web interface (Streamlit or similar) Budget: ≈ $500 Timeline: 2–4 weeks Important: This is a prototype project, not a large production system. Simple solutions using existing AI models are welcome. Please include: - examples of similar projects - your suggested technical ...
...System Test strategies using historical data. Show results such as: Win rate Profit/loss Drawdown Number of trades Live Analysis Run the strategy on live data. Generate signals or alerts when conditions are met. Dashboard A simple web dashboard where I can: View charts Monitor signals See performance statistics Control strategy settings Suggested frameworks: Python Pandas / NumPy Streamlit or Flask dashboard Strategy Customization The tool should allow modification of: Indicators Entry rules Exit rules Risk management settings Documentation Clear installation instructions Code comments Basic user guide The goal is to create a system that can analyze historical data, run backtests, and display results in a simple dashboard. Core Requirements Data Coll...
Explainable AI Models for Any topic with novelty Project Des...visualizations), data preprocessing (handling missing data, encoding categorical features, scaling numeric data, removing outliers), and feature selection. Train and compare multiple ML models with cross-validation and hyperparameter tuning, and include explainability using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). Deploy the system with a Streamlit web UI where users can upload datasets, select the target column, run preprocessing, train models, view EDA visualizations, see model results, make predictions, and download the trained model and pipeline. You must also suggest and add additional technologies, models, or ideas to improve the project’s novelty an...
I have already trained and deployed a Logistic Regression model in Streamlit that classifies breast-tumour samples as malignant or benign. What I need now is a polished data-visualization layer so users can quickly grasp how each feature influences the prediction. My immediate focus is on bar-chart visualisations. I want clear, well-labelled bars that compare malignant vs. benign distributions, show feature importances, and surface any other insight you think adds value. The work should plug straight into my current Streamlit app and read from the same Pandas DataFrame I am already passing to the model. Although the main task is visualisation, I am also experimenting with feature selection, so if your code can be structured in a way that makes it easy to toggle feature su...
...recognise plant diseases, and then serves the prediction through a Streamlit interface. Because I do not yet have the images, the first task is to identify and download a suitable, well-labelled dataset from Kaggle. Feel free to compare a few candidates, but the final choice should give good class balance and enough samples per disease category. Once the data is in place, walk through exploratory data analysis, preprocessing, and augmentation inside a Jupyter notebook. From there, build and tune a convolutional neural network (TensorFlow / Keras or PyTorch are both fine) and report the usual metrics plus a confusion matrix so I can judge class-wise performance. When the model is satisfactory, save it and wrap inference in a clean Streamlit app where a user uploads a singl...
... operational costs, market multiples) to project ROI. * **Exit Scenario Analysis:** Build logic to test different exit criteria (e.g., acquisition at Year 3 vs. Year 5) and their impact on terminal value. * **Data Integration:** Fetch and process market benchmark data via APIs or web scraping to keep valuation multiples realistic. * **Visualization:** Develop a lightweight dashboard (using **Streamlit** or **Dash**) to visualize "Best Case," "Base Case," and "Worst Case" profit scenarios. * **Documentation:** Ensure the code is modular and well-documented so it can be updated as our strategy evolves. ### **Required Skills** * **Core Python:** Expert-level proficiency in Python 3.x. * **Data Libraries:** Strong experience with **Pandas** and **NumPy...
...& Scoring: The agent must provide a "Scorecard" after each session, grading me on: Technical Accuracy, STAR Method Structure, and Cultural Fit. Long-Term Memory: The agent must store a history of my mistakes and focus future sessions on weak areas (using a Vector DB like Pinecone or ChromaDB). Specific Deliverables (Must provide to complete project): Functional Web/Local App: A clean UI (Streamlit, Chainlit, or React) where I can upload my CV and the specific Job Description. Configurable Knowledge Base: A module where I can upload PDFs/URLs (Whitepapers, Case Studies) that the agent will use as its "Source of Truth." The "Prompt Engineering" Library: A documented set of System Prompts used for each interview persona (The "Hard" T...
I have already deployed a full Streamlit application that predicts loan approvals in real time (live demo: , source: ). The pipeline currently includes Logistic Regression, K-Nearest Neighbors, and Naive Bayes models with standard scaling and the usual EDA-driven feature engineering. What I want now is a measurable lift in overall model performance, with the F1-score as the guiding metric. Feel free to explore more advanced algorithms (e.g., Gradient Boosting, XGBoost, LightGBM, calibrated ensembles, or even a tuned version of my existing classifiers) as long as they integrate cleanly with the existing Python | Pandas | NumPy | Scikit-learn stack and can be surfaced through the current Streamlit front-end. Key points you should address •
...the genomic report and basic demographics, after which your agent populates both front ends automatically. • Privacy and traceability: every recommendation should carry a link back to the paper/guideline line it came from. I am open to the model stack—GPT-4, Claude, or an open-source LLM fine-tuned on COSMIC—so long as you explain why it meets medical-grade safety. A lightweight React (or Streamlit) front end is fine; just keep the code modular so I can hand it to our IT team later. Acceptance criteria • I can upload a de-identified sample report and immediately see the dual views populate without error. • At least three mutations receive plain-English patient explanations under 200 words each. • The physician tab lists one evidence-backe...
...'s API to push those emails into their sending platform. Instantly handles all the actual delivery, warming, rotation, and tracking. Your script loads emails in and pulls analytics back out — opens, clicks, replies, bounces.A Calendly webhook or polling integration that detects when a prospect books an appointment and logs it to the database.A simple read-only customer dashboard (Retool, Streamlit, or Bubble) where the end user logs in and sees today's stats — emails sent, opened, replies, appointments booked — plus a 30-day trend chart and a recent activity runs via cron jobs on a DigitalOcean or Hetzner VPS. Scraper runs at 2am, email verification at 4am, email generation at 6am, sending at 8am, analytics polling every 60 minutes. The system must ru...
...Runs on VPS (24/7 optional) Logs all trades and errors Has basic dashboard or whatsapp alerts Technical Requirements Python 3 kiteconnect library WebSocket live data Modular code structure Error handling Daily access token auto generation Clean documentation Deployment Deploy on: AWS / DigitalOcean / Indian VPS Must include: Setup guide Backup instructions Logging system Phase 2 Dashboard (Streamlit) Backtesting engine Capital allocation logic Multi-strategy switch WhatsApp / Telegram trade alerts Performance analytics Deliverables Fully working source code Setup documentation 2 weeks support after delivery Strategy explanation Backtesting results Budget Open to: Fixed price OR Milestone-based payment Screening Question (Very Important) Please answer: Have you built system...
...the Google Sheets data into a searchable vector database. Preference for free/local options: Chroma, Pinecone, or Weaviate. AI Engine Integration: Utilize a fast, modern AI engine for the RAG pipeline. Options: Groq, LlamaIndex (preferred), or OpenAI. Chat Widget Development: Embed a clean, responsive chat widget on a simple landing page I will need for the project. Technology Recommendation: Streamlit, Tidio, or custom HTML/JS (developer's recommendation). Source Attribution: Implement a "Show sources" functionality. When an answer is provided, the user must be able to see the specific rows (titles, URLs, and/or summaries) from the Google Sheet that the answer came from. Nice-to-Have Features Mobile-friendly design for the widget and landing page. Basic privacy co...
...checking, normalisation of party names/regions and basic predictive analysis. Leveraging APIs such as OpenAI, Claude or Vertex for cleaning and pandas / scikit-learn for statistical work is fine; I’m happy to fund premium tiers where that improves reliability and speed. • Visual output – the cleaned dataset should feed straight into a powerful dashboard ( Power BI, Tableau, Florish, Looker Studio, Streamlit, Plotly Dash, Superset or similar) that automatically refreshes and renders laser-sharp bar charts, line charts and comparative graphs. Quick download options for CSV/Excel will be required. Acceptance criteria 1. A single command or deploy-to-cloud button stands up the full workflow. 2. Scraper demonstrates <1 min lag behind the source during live ...
Build Full Stack API with FastAPI, PostgreSQL & Streamlit UI You said: Uploaded image Give me matter for this ChatGPT said: Good. This is important. If you write a weak description, you’ll attract low-quality freelancers. If you write clearly, you get serious professionals. I’ll give you a strong, clear project description you can directly paste. Option 1: FastAPI + PostgreSQL + Streamlit Project Project Title Idea: Build Full Stack API with FastAPI, PostgreSQL & Streamlit UI I need a developer to build a full-stack application using FastAPI, PostgreSQL, and Streamlit. Project Requirements: • Build REST APIs using FastAPI • Connect to PostgreSQL database • Implement full CRUD operations (Create, Read, Update, Dele...
...student-friendly interface (Web-based local UI or Desktop App) where students can type questions and get instant feedback. * Scalability: The ability to easily add or update the curriculum folders. Technical Preferences (Suggested) * Backend: Python (LangChain or LlamaIndex) * Model Management: Ollama, LocalAI, or GPT4All * Vector Database: ChromaDB or FAISS (must be local/persistent) * Frontend: Streamlit, Gradio, or a lightweight React app Ideal Candidate * Experience with Local Large Language Models (LLMs). * Proven track record building RAG pipelines. * Familiarity with hardware limitations for offline AI. * Experience in the EdTech space is a plus. Tips for your post: * Hardware: Decide what the students will use (e.g., "It must run on a laptop with 8GB RAM&qu...
I need an interactive dashboard built in Streamlit that lets end-users explore time-series data coming from three different sources—raw CSV uploads, existing relational databases, and live API endpoints. The app should read, clean, and merge these feeds on the fly, then offer clear visual insights through line charts, area charts, and any other plots that make trends, seasonality, and anomalies obvious. Under the hood I expect well-structured, reusable Python code that leans on pandas for manipulation, SQLAlchemy (or similar) for database access, and a lightweight requests layer for the APIs. Caching, session-state handling, and responsive layout controls are important so the interface feels fast even as data volumes grow. Deliverables • Streamlit app folder with...
...produce them as files. •Create visuals for the web report or individual reports as needed. •Elaborate any correlation between variables (if and when applicable) •Apply linear regression to predict the Digital maturity level for an input variable. •Show top 10 best vs 10 worst cases in dashboard. •Final result should look like this (we want this or similar, maybe streamlit) Deliverables: Both Excel Files; Python analysis code; Sample reports (5 pdf or word); web dashboard with items as per reference. Below you have sample data from previous reports delivered, such as: 1. Sample questionnaire from DMAT tool 2. Sample from DMAT tool response 3. Sample Dashboard from Digitalmaturity,org 4. Sample report from Gartner (for reference on models and visualizat...
I am looking for a tutor who can help me understand, in a comprehensive and step-by-step manner, how to deploy a production-ready dashboard that displays the results of a model. My current stack includes: Google Cloud Platform (GCP) Vertex AI Workbench (where I trained the model) Streamlit (for the dashboard) Git and GitHub (for version control) I already have: A trained model in Workbench The .py files developed A structured and synchronized GitHub repository The intention to deploy everything in GCP However, I am not only looking for help with deployment — I want to fully understand the technical process behind it, including: How to properly structure the project for production. How to connect the trained model to the dashboard. The best architecture choice in ...
...need an interactive dashboard built in Streamlit that lets end-users explore time-series data coming from three different sources—raw CSV uploads, existing relational databases, and live API endpoints. The app should read, clean, and merge these feeds on the fly, then offer clear visual insights through line charts, area charts, and any other plots that make trends, seasonality, and anomalies obvious. Under the hood I expect well-structured, reusable Python code that leans on pandas for manipulation, SQLAlchemy (or similar) for database access, and a lightweight requests layer for the APIs. Caching, session-state handling, and responsive layout controls are important so the interface feels fast even as data volumes grow. Deliverables • Streamlit app fold...
...Sales. Growth Potential: Year-over-year growth in PAT. 3. Reporting & Output Summary Dashboard: A clean visual showing if the company is "Undervalued" or "Overvalued" based on the Intrinsic Value vs. current Spot Price. Risk Flags: Highlight if OCI shows significant losses (like pension liabilities or currency drops) that might be hidden from the main P&L. Technical Preferences Platform: A Streamlit Web App (Python), a specialized Google Sheet with AppScript, or a simple React dashboard. Simplicity: The goal is execution and functionality, not a perfect UI. It must work "out of the box." Deliverable A working link (URL) or a source file that I can run locally to demonstrate the tool for a video presentation....
...student-friendly interface (Web-based local UI or Desktop App) where students can type questions and get instant feedback. * Scalability: The ability to easily add or update the curriculum folders. Technical Preferences (Suggested) * Backend: Python (LangChain or LlamaIndex) * Model Management: Ollama, LocalAI, or GPT4All * Vector Database: ChromaDB or FAISS (must be local/persistent) * Frontend: Streamlit, Gradio, or a lightweight React app Ideal Candidate * Experience with Local Large Language Models (LLMs). * Proven track record building RAG pipelines. * Familiarity with hardware limitations for offline AI. * Experience in the EdTech space is a plus. Tips for your post: * Hardware: Decide what the students will use (e.g., "It must run on a laptop with 8GB RAM&qu...
...(MVP) Project Overview: I am looking for an AI/ML developer to build a functional prototype of a security system designed to detect "Digital Arrest" scams. The system needs to analyze video and audio inputs in real-time (or near real-time) to identify deepfakes, threatening language, and fake law enforcement visuals. Key Features Required (The Scope): I need a desktop-based prototype (Python/Streamlit or similar) that can process a sample video feed or live webcam input and perform the following: * Audio Threat Detection (NLP): * Transcribe audio in real-time (using OpenAI Whisper or Google Speech-to-Text). * Detect specific scam keywords/intents (e.g., "money laundering," "CBI," "narcotics," "arrest," "isolate yoursel...
Project Title: Build a Multi-Modal AI Productivity Suite (Meeting & Document Intelligence) Project Description I am seeking a developer to collaborate on a high-level AI productivity tool. The goal is to create a system that can pr...Retrieval-Augmented Generation system allowing users to upload large PDF/DOCX files and "chat" with the data for specific facts. Module 3: Sentiment & Intent Analysis: A dashboard component that tracks the "mood" of a conversation or document. Integration: A clean, functional API or Streamlit-based frontend to tie these features together. Required Technical Stack: Language: Python AI Frameworks: LangChain or LlamaIndex Transcription: OpenAI Whisper or AssemblyAI Database: Vector Databases (ChromaDB, Pinecone, or FAISS)...
