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README.md

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+# 🧠 **Cloud-Based Medical QA System**
+### ☁️ *AWS-Integrated Machine Learning Project*
+
+---
+
+## 📋 **Project Overview**
+This project demonstrates a **cloud-deployed Machine Learning (ML)** application built to answer **medical-related questions** using a fine-tuned QA model.  
+The system leverages **AWS services** to ensure **scalability**, **accessibility**, and **secure management** of data and infrastructure.
+
+The model was trained and deployed on an **Amazon EC2** instance, with data securely stored in an **Amazon S3** bucket.  
+Access permissions and security were managed using **AWS IAM (Identity and Access Management)**.
+
 ---
-title: Chatbot
-emoji: 👀
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-short_description: A medical Chatbot trained on 17k disease based questions
+
+## ☁️ **AWS Services Used**
+
+### 🔐 **1. AWS Identity and Access Management (IAM)**
+**Purpose:**
+- 🧾 Created and managed secure access permissions for different AWS resources.  
+- 👤 Configured a custom IAM user/role with limited access to S3 and EC2.  
+- 🛡️ Followed the principle of *least privilege* to ensure minimal security risks.  
+- 🔑 Used IAM for safe credential management during local access testing.  
+
+**Alternative (Not Implemented):**
+- 🧰 *AWS Secrets Manager* for automatic credential rotation.  
+
+**Reason:** Not necessary for small-scale academic deployment and would increase complexity and cost.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+### 🗃️ **2. Amazon S3 (Simple Storage Service)**
+**Purpose:**
+- ☁️ Used for storing the `cleaned_medquad.csv` dataset, providing a reliable, cloud-based data source for the ML model.  
+- 📤 The dataset was uploaded manually to an S3 bucket.  
+- 🗂️ Served as a centralized, secure data storage solution.  
+
+**Alternative (Not Implemented):**
+- 🔗 Direct programmatic access using the **Boto3 SDK** to read data from S3 within the EC2 app.  
+
+**Reason:** For demonstration purposes, manual upload was sufficient, and integration was skipped to focus on showcasing AWS setup.
+
+---
+
+### 💻 **3. Amazon EC2 (Elastic Compute Cloud)**
+**Purpose:**
+- 🚀 Used to host and run the ML model and **Gradio interface**.  
+- ⚙️ Configured a `t2.medium` Ubuntu instance for deployment.  
+- 🧩 Executed Flask/Gradio app and tested successfully via public URL.  
+- 🔍 Verified full model functionality and response generation.  
+
+**Alternative (Not Implemented):**
+- 🪶 *AWS Lambda* or *AWS SageMaker* for serverless or managed ML hosting.  
+
+**Reason:** EC2 was ideal for this scale and provided better control over dependencies and environment setup.
+
+---
+
+## 🚀 **Deployment Flow**
+1. 🧺 Dataset uploaded to **S3 bucket**  
+2. 🔐 IAM role created for **secure access management**  
+3. 💻 **EC2 instance** launched and configured  
+4. 🤖 ML application (Flask + Gradio) deployed and tested  
+5. 📈 Logs and results verified on **terminal and Gradio public link**
+
+---
+
+## 🧩 **Tech Stack**
+| Layer | Tools & Technologies |
+|-------|----------------------|
+| 🧠 **Backend** | Python (Flask, Gradio) |
+| ☁️ **Cloud** | AWS EC2, S3, IAM |
+| 🧰 **Libraries** | Pandas, Transformers, Scikit-learn |
+| 📊 **Dataset** | Medical QA Dataset (`cleaned_medquad.csv`) |
+
+---
+
+## 📸 **Screenshots**
+📁 Available in the `/screenshots` folder:  
+1. 🧑‍💻 IAM Roles and Permissions  
+2. 🪣 S3 Bucket with Uploaded Dataset  
+3. 💻 EC2 Instance Configuration  
+4. 🧾 Terminal Log (Model Running Successfully)  
+5. 🌐 Gradio Interface Screenshot  
+
+---
+
+## ⚙️ **Future Integration**
+Planned upgrades for the next version:
+1. 🤖 Automate dataset retrieval using **Boto3**  
+2. 🔐 Integrate **IAM role-based S3 access** into code  
+3. 🧱 Deploy the model using **AWS SageMaker** for managed ML  
+4. 💾 Store model responses in **Amazon RDS** for persistence  
+
+---
+
+## 🧹 **Resource Management**
+All AWS resources (**EC2**, **IAM**, and **S3**) have been **safely terminated** after testing to prevent unnecessary billing.  
+
+---
+
+## 👨‍💻 **Author**
+**Aaditya Arvind Ramrame**  
+🌩️ *Cloud and Machine Learning Enthusiast*  
+📧 [aadityaramrame@gmail.com](mailto:aadityaramrame@gmail.com)  
+🔗 [GitHub Profile](https://github.com/Aadityaramrame)
+
+---
+⭐ *“Bringing Machine Learning to the Cloud — One Service at a Time.”* ☁️