Streamlit trabalhos

...descartei etapas de classificação ou análise isolada; meu foco é, exclusivamente, a visualização interativa em formato de dashboards. A ideia é conectar o modelo BERT aos meus conjuntos de dados de gestão de projetos, processá-los e exibir informações dinâmicas — por exemplo, progresso, recursos e prazos — em um painel web responsivo. Estou aberto a sugestões de layout e de ferramentas (Python, Streamlit, Dash, Power BI, Tableau ou similares), desde que o resultado seja intuitivo e fácil de atualizar a partir de arquivos CSV ou de uma API interna. Entrego a você os dados brutos e o acesso ao servidor; espero, ao final, o repositório de código, ins...

...consolidadas • Decisões que ratifiquem entendimentos firmados nos tribunais superiores ou locais • Interface amigável (web ou plugin) • Estrutura modular Tecnologias desejadas: • Python (preferência) ou Node.js • OpenAI API (ChatGPT-4 ou 4o) • API do Manus Docs (tenho acesso) • APIs como Codilo ou JUIT Rimor (posso fornecer acesso) • Framework para frontend leve (Electron ou Streamlit opcional) Entregas esperadas: 1. Protótipo funcional 2. Consulta de jurisprudência integrada 3. Captação de publicações recentes 4. Integração com Manus 5. Interface mínima de uso 6. Manual técnico Preferência para quem já ...

...consolidadas • Decisões que ratifiquem entendimentos firmados nos tribunais superiores ou locais • Interface amigável (web ou plugin) • Estrutura modular Tecnologias desejadas: • Python (preferência) ou Node.js • OpenAI API (ChatGPT-4 ou 4o) • API do Manus Docs (tenho acesso) • APIs como Codilo ou JUIT Rimor (posso fornecer acesso) • Framework para frontend leve (Electron ou Streamlit opcional) Entregas esperadas: 1. Protótipo funcional 2. Consulta de jurisprudência integrada 3. Captação de publicações recentes 4. Integração com Manus 5. Interface mínima de uso 6. Manual técnico Preferência para quem já ...

We are looking for an experienced Python developer to build a Streamlit web application

...data”—size, transaction volume, geography, existing controls—and prepare it as the training / inference set. • Model: tune an appropriate NLP or rules-augmented model so that, given the company profile, it returns the single best-fit framework plus a short justification. Python with scikit-learn, spaCy, or a lightweight transformer is fine; I’m open to your suggestions. • Interface: a minimal GUI (Streamlit or similar) where users paste or upload company parameters and receive the framework recommendation instantly. • Validation: design measurable accuracy tests—cross-validation, confusion matrix, precision / recall—so I can cite quantitative performance in my paper. • Documentation: comment the code thoroughly and s...

...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 ...

Se busca un experto en Inteligencia Artificial Generativa para colaborar como socio tecnológico en el des...CrewAI para el desarrollo de agentes. * Bases de Datos Vectoriales: Conocimiento y experiencia práctica con Pinecone, Weaviate, Qdrant o ChromaDB. * Modelos de Lenguaje (LLMs): Experiencia en la integración de APIs de OpenAI/Anthropic y en la ejecución de modelos locales (Ollama/HuggingFace). * Prototipado de Interfaces: Habilidad para crear prototipos rápidos utilizando herramientas como Streamlit o Chainlit. Perfil del Candidato Ideal: * Idioma: Español Nativo. La comunicación técnica y fluida en español es un requisito excluyente para este proyecto. * Actitud proactiva y disposición para ense&...

...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 ...

Se busca un experto en Inteligencia Artificial Generativa para colaborar como socio tecnológico en el des...CrewAI para el desarrollo de agentes. * Bases de Datos Vectoriales: Conocimiento y experiencia práctica con Pinecone, Weaviate, Qdrant o ChromaDB. * Modelos de Lenguaje (LLMs): Experiencia en la integración de APIs de OpenAI/Anthropic y en la ejecución de modelos locales (Ollama/HuggingFace). * Prototipado de Interfaces: Habilidad para crear prototipos rápidos utilizando herramientas como Streamlit o Chainlit. Perfil del Candidato Ideal: * Idioma: Español Nativo. La comunicación técnica y fluida en español es un requisito excluyente para este proyecto. * Actitud proactiva y disposición para ense&...

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...

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...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)...

...* No duplicate or delayed alerts 6. Interactive Charting * Display Point & Figure charts interactively * UX comparable to TradingView or similar * Must support: * Zoom / pan * Timeframe switching * Live updates 7. Tech Stack (Preferred, Not Mandatory) * Backend: Python (FastAPI / Flask / Django) * WebSockets: KiteTicker * Frontend: * React / Vue / plain JS * OR Python-based UI (Streamlit acceptable only if justified) * Hosting:AWS EC2 (Windows) * Alerts: Telegram Bot API 8. Alert based trades * Based on the alerts(buy/sell), proceed to trigger the trade. * The trade should be overnight all the time. * Should have the view to lock the lots and continue the trades without any stops. * During expiry if the trade is on overnight, exit that on the day or one day b...

Build, connect, and run AI-powered apps instantly using Streamlit and FastAPI — a lightweight accelerator for creating AI apps with minimal setup AI App Builder - Agentic Pipeline Architecture Overview This project uses an agentic (LangGraph-inspired) architecture to generate modular applications from user queries. The core pipeline consists of specialized agents for planning, editing, verifying, and assembling code for frontend, backend, and logic modules. the app looks good but i want some modifications basically i want that when user enters it requirements then it will give Refined Requirements: App Requirements Summary App Overview Core Features Technologies User Interface Data Requirements Assumptions like this which we can edit according to our need or if we are ok with ...

Build, connect, and run AI-powered apps instantly using Streamlit and FastAPI — a lightweight accelerator for creating AI apps with minimal setup AI App Builder - Agentic Pipeline Architecture Overview This project uses an agentic (LangGraph-inspired) architecture to generate modular applications from user queries. The core pipeline consists of specialized agents for planning, editing, verifying, and assembling code for frontend, backend, and logic modules. the app looks good but i want some modifications basically i want that when user enters it requirements then it will give Refined Requirements: App Requirements Summary App Overview Core Features Technologies User Interface Data Requirements Assumptions like this which we can edit according to our need or if we are ok...

Build, connect, and run AI-powered apps instantly using Streamlit and FastAPI — a lightweight accelerator for creating AI apps with minimal setup AI App Builder - Agentic Pipeline Architecture Overview This project uses an agentic (LangGraph-inspired) architecture to generate modular applications from user queries. The core pipeline consists of specialized agents for planning, editing, verifying, and assembling code for frontend, backend, and logic modules. the app looks good but i want some modifications basically i want that when user enters it requirements then it will give Refined Requirements: App Requirements Summary App Overview Core Features Technologies User Interface Data Requirements Assumptions like this which we can edit according to our need or if we are ok...

Project Title: Python ML Developer...details (e.g., specific loops, slants, and imperfections) from the sample image. It should look like real human writing, not a computer font. What You Need to Do: Select & Implement the Best Model: You have the creative freedom to choose the architecture (GANs, Diffusion Models, or Style Transfer). I am looking for the most realistic output. Build a Simple Interface: A basic local web page (Flask/Streamlit) to upload the image and generate text. Output Format: The system should generate high-resolution images (and preferably vector strokes if possible). Requirements: Experience with PyTorch/TensorFlow. Knowledge of Generative AI (GANs, Diffusion, or Transformer-based style transfer). Ability to work with Image Processing (OpenCV) to clean up ...

...Python codebase + README b) Validation report (results, risks, limitations) c) “Plain English” summary for business stakeholders Tech Stack (Preferred) 1. Python 3.10+ 2. PyTorch 3. PINN tooling: DeepXDE or custom PINN in PyTorch 4. pandas, numpy, scikit-learn 5. PDF extraction: pdfplumber + (Camelot/Tabula if needed) Optional: MLflow or W&B for experiment tracking Optional: FastAPI or Streamlit for demo Required Experience (Non-Negotiable) Proven experience with time-series forecasting on messy real-world datasets Hands-on experience with Physics-Informed ML / PINNs (show work, not theory) Strong Python engineering (clean modular code, reproducible experiments) Experience building validation frameworks (train/test split by site, not random rows) ...

...dataset that I will supply at project start. The stack is already chosen: Streamlit for the interactive UI, FastAPI as the service layer, a SQLite store for both raw data and model artefacts, Scikit-learn for data preparation, and a Temporal Fusion Transformer (TFT) as the core model. Visual insights should be delivered through Matplotlib or seaborn charts embedded directly in the Streamlit app. Key points to hit • Cleanly ingest the CSV, write it into SQLite, and expose CRUD endpoints through FastAPI. • Build the full forecasting pipeline—feature engineering, training/validation splits, hyper-parameter tuning, and model persistence. • Serve monthly forecasts via a REST endpoint and display them in Streamlit alongside historical trends...

...the only input in the first release, so your signal-processing and machine-learning choices must squeeze maximum insight from that single source. I’m open to Python (NumPy, SciPy, scikit-learn, TensorFlow) or MATLAB toolchains as long as the final product is easy for me to retrain with new runs. Deliverables • Source code with clear comments and a short setup guide • A lightweight dashboard (Streamlit, Dash, or similar) showing live health indicators, trend plots and a simple traffic-light status • A sample dataset and step-by-step notebook that reproduces your results • Brief report explaining feature extraction, model selection and validation results Acceptance criteria: the model should detect at least 90 % of seeded failure events from the sample s...

...• Exportable outputs: • CSV • PDF Optional: • Simple web dashboard using Streamlit or Flask • Mobile-friendly view ⸻ 7. Cost Optimization The solution must: • Prefer free or low-cost APIs • Use open-source libraries: • Python • Pandas • Scikit-learn • TensorFlow / PyTorch • Use paid APIs (ChatGPT/Gemini) selectively only where necessary ⸻ Deliverables 1. Fully functional ML pipeline 2. Clean and documented source code 3. Trained models 4. Data ingestion scripts 5. Model evaluation report 6. Deployment-ready setup 7. User guide ⸻ Preferred Tech Stack • Python • Pandas / NumPy • Scikit-learn • TensorFlow or PyTorch • HuggingFace Transformers • OpenAI / Gemini AP...

Project Overview: I am looking for a developer to build a simple web-based AI "wrapper." The Workflow: Upload: User uploads a PDF court document. AI Processing: The system reads the PDF (including checked boxes) and sends the text to an LLM (OpenAI or Claude). Display: A simple split-screen view: Left ...Requirements: Speed of Build: I want this up and running quickly. It doesn't need to be lightning-fast (processing time is flexible), but the build should be straightforward. No Database: No logins, no accounts, and no saving data. This is session-based for privacy. Clean UI: Simple, professional, and mobile-friendly. -> I might provide the UI Tooling: You choose the simplest stack (, Streamlit, etc.) that allows me to easily update the AI instructions (p...

Hi, I am looking for a Google Cloud Platform...the PORT=8080 environment variable within the allocated timeout." What I need: Review my current configuration (I can share my screen or provide access). Identify why the container is not binding to the correct port (8080). Fix the issue so the application deploys successfully and is accessible via the public URL. Tech Stack involved: Google AI Studio Google Cloud Run Docker / Python (likely Streamlit or Flask, depending on the AI Studio export). Please apply if: You have experience with Google Cloud Run troubleshooting. You understand how to configure Docker containers to listen on the $PORT environment variable. You can communicate clearly in English (or Spanish). Looking for a quick turnaround as this should be a configuratio...

...only input in the first release, so your signal-processing and machine-learning choices must squeeze maximum insight from that single source. I’m open to Python (NumPy, SciPy, scikit-learn, TensorFlow) or MATLAB toolchains as long as the final product is easy for me to retrain with new runs. Deliverables • Source code with clear comments and a short setup guide • A lightweight dashboard (Streamlit, Dash, or similar) showing live health indicators, trend plots and a simple traffic-light status • A sample dataset and step-by-step notebook that reproduces your results • Brief report explaining feature extraction, model selection and validation results Acceptance criteria: the model should detect at least 90 % of seeded failure events from the samp...

Project Description I am the manager of a manufacturing plant building "Paideia," an AI maintenance assistant. I need a Python Backend Developer to build a Dockerized RAG (Retrieval-Augmented Generation) System. The Core Goal: I need a system where I can personally upload and manage a library of 100+ te...Backend: Python (FastAPI). Infrastructure: Google Cloud Platform (Cloud Run). Database: Dockerized Vector DB (Chroma/Qdrant) with Persistent Storage. Queue: Async Task Queue (for handling large uploads). Deliverables Dockerized Source Code: Ready to deploy. API Endpoints: For Upload (Bulk), Status Checking, and Deletion. Basic "Admin" UI (Optional but Preferred): A simple HTML/Streamlit page to drag-and-drop files and see a progress bar (e...

...already have a first-cut codebase that an AI generated for a small desktop-web hybrid dashboard. It mixes Streamlit with a PySide/PyQt front end, runs on Python 3, and pulls Poppler and OpenCV in the background for PDF and image handling. What I need now is a developer who can step in, clean the code, and make the whole thing run exactly as intended. Core goal Turn the existing prototype into a smooth interactive dashboard that can visualise data coming from CSV files, live database connections, and a couple of light-weight REST APIs. The layout and widgets are sketched out; several functions compile but don’t yet talk to each other the way they should. Scope of work • Refactor the Streamlit and PySide/PyQt layers so they share state seamlessly (no duplica...

Hello , Prefer Developer from - Vietnam , China , Singapore - Task Detail - I have existing Demo ( Logic ) made in streamlit , You will have to convert exact same logic in Fast API. While convert Fast API you have to follow or convert in to current structure of API (which i am using in existing chatbot ) Execution's time - Currently its taking too much time to load resposne due to multipel hit on API , some of hits might be need to change model. Demo URL is connected with demo data . Fast APi need to connect with live ES. Final Delivery would be API , i will test on Postman. Must follow the structure of demo code. you have to work on GitHub. Budget - 10 ,000 INR for this task. Timeline - 1 - 2 days maximum

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...(FFT/Spectral Analysis) and what framework will you use for anomaly detection on the Raspberry Pi? 3. Safety Interface: Since we are not using a PLC, how will you safely control the 0.5kW AC motor using the Pi's GPIO? (Please specify your choice of contactors or high-power relays). 4. Web Dashboard: One of the core requirements is a web-based display. Please specify the stack you would use (e.g., Flask, Streamlit, or Node-RED) to visualize real-time sensor data, health status, and "Defect Detected" alerts. 5. Proof of Competency: Share a brief summary or link to a previous project involving Edge AI or IoT Dashboards. I am looking for a solution that is robust, safe for 220V operation, and provides a clean user interface. Best regards,...

I’m looking to build a small-footprint, AI-driven tool that takes a list of company names from me and returns a ranked, fully-scored spreadsheet of prospects that clearly shows who is most in need of digital marketing help. How I want it to work 1. I drop in a batch of company names (CSV, Google Sheet, or simple text input—whatever is fastest for you to wire up...(CSV or Google Sheet) listing the companies, their individual factor scores, and an overall “needs help” rank. • A quick Loom or written walkthrough showing setup and how to retrain or adjust rules. If you’ve already built lead-scoring or web-scraping automations, let’s talk—I’ll prioritise proven experience with Python scraping libraries, GPT or other LLM APIs, and lightw...