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madhusundar240/smart-ai-healthcare

20PythonAudience · developerComplexity · 3/5Setup · moderate

TLDR

A Python web app that predicts possible diseases from typed, spoken, or image-based symptoms using machine learning, and returns a severity level, dietary advice, precautions, a PDF report, and a map of nearby hospitals.

Mindmap

mindmap
  root((smart-ai-healthcare))
    Inputs
      Typed symptoms
      Voice input
      Image upload
    Outputs
      Disease prediction
      Severity level
      PDF health report
      Nearby hospital map
    Tech Stack
      Python FastAPI
      scikit-learn
      ReportLab
      HTML CSS JS
    Use Cases
      Portfolio project
      Symptom checker
      Health report generation
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Things people build with this

USE CASE 1

Describe symptoms by typing, speaking, or uploading an image to receive a possible disease prediction with a severity level and precaution advice.

USE CASE 2

Generate a downloadable PDF health report summarizing the predicted condition, dietary suggestions, and recommended precautions.

USE CASE 3

Find nearby hospitals on a Google Maps view based on your location after receiving a disease prediction.

USE CASE 4

Study how FastAPI, scikit-learn classification, and a multi-input frontend are wired together as a portfolio or learning project.

Tech stack

PythonFastAPIscikit-learnReportLabJavaScriptHTMLCSS

Getting it running

Difficulty · moderate Time to first run · 30min

Clone the repository, install Python dependencies, start the FastAPI server, and open the frontend HTML in a browser, a Python environment (3.x) is required.

No license information is mentioned in this repository.

In plain English

This is a healthcare web application that tries to predict possible diseases based on symptoms you describe. You can type your symptoms, speak them aloud, or upload an image, and the system uses machine learning models to suggest what condition you might have. The README does not describe the specific models used or how they were trained, only that they rely on symptom and disease datasets. Alongside the disease prediction, the application provides additional outputs: a severity level for the predicted condition, dietary suggestions, precaution advice, and a PDF report summarizing the results. It also integrates with Google Maps to show nearby hospitals based on your location. The application supports multiple languages, which the README lists as a feature but does not detail further. Voice input is handled through the browser, and image-based input is mentioned as a way to describe symptoms visually, though the README does not explain how image analysis works internally. The backend is built with FastAPI, a Python web framework, and the machine learning components use scikit-learn, a common Python library for classification and prediction tasks. The frontend is plain HTML, CSS, and JavaScript. PDF generation uses a Python library called ReportLab. To run it locally, you clone the repository, install Python dependencies, start the FastAPI server, and open the frontend HTML file in a browser. The README is brief and does not cover model accuracy, data sources in detail, or any limitations of the predictions. This appears to be a student or portfolio project rather than a clinical tool.

Copy-paste prompts

Prompt 1
I want to add a new symptom dataset to this smart-ai-healthcare FastAPI app and retrain the scikit-learn model. Walk me through how to swap the dataset and update the prediction endpoint.
Prompt 2
Extend the smart-ai-healthcare app to show a confidence percentage alongside each disease prediction so users can see how certain the model is.
Prompt 3
The PDF report from ReportLab doesn't include the nearby hospital list. Show me how to add a section with the top 3 Google Maps results to the PDF output.
Prompt 4
Add input validation to the FastAPI symptom endpoint so it rejects empty submissions and returns a helpful error message instead of crashing.
Prompt 5
Explain how the voice input in this app works in the browser, trace the JavaScript speech recognition flow from the microphone to the FastAPI prediction endpoint.
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