🤖 Intent-Based Chatbot with Streamlit

This repository contains two intent-based chatbots built using Natural Language Processing (NLP) techniques. One chatbot is deployed using Localtunnel, and the other is deployed directly through a Streamlit account.

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🌐 View the Deployed Chatbot

To interact with the chatbot deployed using Streamlit, click the link below:
🔗 Chatbot URL

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🛠️ Running and Setup Instructions

🧪 For Localtunnel Deployment

To view and run the chatbot deployed using Localtunnel, follow the instructions in the provided Jupyter Notebook (.ipynb). The notebook contains step-by-step instructions for:
1️⃣ Setting up the environment
2️⃣ Preparing the data and code 3️⃣ Running the chatbot using Localtunnel on Streamlit

🌟 For Streamlit Deployment

To run the chatbot with a Streamlit account:

1. ✍️ Create an account on the Streamlit website.
2. 📁 Copy the app.py, requirements.txt, and intents.json files to a GitHub repository.
3. 🔧 Go to your Streamlit account, and click on Create App.
4. 🔗 Connect your GitHub repository and fill out the required fields.
5. 🚀 Streamlit will fetch the details from GitHub and generate a public URL for your app.

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🧩 Code Explanation

1️⃣ Data Loading and Structure

• Patterns: User input phrases (e.g., "Hello", "How are you?").
• Tags: Intent labels (e.g., "greeting", "goodbye").
• Responses: Predefined chatbot responses for each tag.

2️⃣ Data Preprocessing

• 📝 TF-IDF Vectorization: Converts patterns into numerical data.
• 🔢 Label Encoding: Converts intent tags into numerical values.

3️⃣ Machine Learning Model Training

• 🤖 Uses a Random Forest Classifier for intent classification.
• 📈 Predicts user intent and retrieves a relevant response.

4️⃣ Streamlit Interface

• 💬 Text Input: Users type their messages.
• 🗂️ Chat History: Displays conversation logs.
• 📊 Model Evaluation: Includes accuracy metrics and a classification report.

5️⃣ Deployment

• 🌍 Localtunnel for public URL generation.
• 🚀 Streamlit for seamless web app deployment.

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🛠️ Troubleshooting

⚠️ Bad Gateway Error (Localtunnel)

• Solution: Restart Localtunnel or reload the URL.

⚠️ Issues with Streamlit Deployment

• Ensure requirements.txt and intents.json are properly linked in the GitHub repository.

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📋 Requirements

• Google Drive (account for storing files)
• Google Colab (Any Python environment supporting Jupyter notebooks)
• 🖥️ Python 3.6+
• 🌐 Streamlit (For running the interactive app)
• 🧠 NLTK (for natural language processing)
• 📊 scikit-learn (for ML model training)
• 🚀 localtunnel (for public URL generation)
• 💾 A GitHub repository with the required files.

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🚀 Steps to Deploy Streamlit App

1. Prepare Files:

   • app.py: Main Streamlit app.
   • intents.json: Chatbot data file.
   • requirements.txt: Python dependencies.

2. Push Files to GitHub:

   • Upload the above files to a GitHub repository.

3. Create a Streamlit App:

   • Visit Streamlit.
   • Click Create App.
   • Select your GitHub repository.
   • 🎉 Streamlit will generate a public URL for your chatbot.

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🌟 Enjoy chatting with the bot! 🤖💬