Update README.md

README.md

@@ -1,168 +1,168 @@
----
-title: News Summarizer
-emoji: 👁
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 5.22.0
-app_file: app.py
-pinned: false
-short_description: An app for summarizing news articles on orgs.
----
-
-# News Summarization and Text-to-Speech Application
-
-## Overview
-This project is a web-based application that extracts key details from multiple news articles related to a given company, performs sentiment analysis, conducts a comparative analysis, and generates a text-to-speech (TTS) output in Hindi.
-
-## Features
-- **News Extraction**: Scrapes and displays at least 10 news articles from The New York Times and BBC.
-- **Sentiment Analysis**: Categorizes articles into Positive, Negative, or Neutral sentiments.
-- **Comparative Analysis**: Groups articles with most semantic similarity. Then compares the groups to derive insights on how a company's news coverage varies.
-- **Text-to-Speech (TTS)**: Converts the summarized sentiment report into Hindi speech.
-- **User Interface**: Provides a simple web-based interface using Gradio.
-- **API Integration**: Implements FastAPI for backend communication.
-- **Deployment**: Deployable on Hugging Face Spaces.
-
-## Tech Stack
-- **Frontend**: Gradio
-- **Backend**: FastAPI
-- **Scraping**: BeautifulSoup
-- **NLP**: OpenAI GPT models, LangChain, Sentence Transformers
-- **Sentiment Analysis**: Pre-trained Transformer model
-- **Text-to-Speech**: Google TTS (gTTS)
-- **Deployment**: Uvicorn, Hugging Face Spaces
-
----
-
-## Installation and Setup
-
-### 1. Clone the Repository
-```bash
-git clone https://github.com/Senzen18/News-Summarizer.git
-cd News-Summarizer
-```
-
-### 2. Install Dependencies
-Ensure you have Python 3.8+ installed. Then, run:
-```bash
-pip install -r requirements.txt
-```
-
-### 3. To run Fast API endpoints
-Start the FastAPI backend:
-```bash
-uvicorn api:app --host 127.0.0.1 --port 8000 --reload
-```
-
-### 4. To run the both Gradio and Fast API
-Start the FastAPI backend:
-```bash
-gradio app.py
-```
-
-### 5. Access the Application
-Once started, access the Gradio UI at:
-```
-http://127.0.0.1:7860
-```
-
----
-
-## API Endpoints
-
-### 1. Fetch News
-**GET** `/news/{company_name}`
-- Fetches the latest articles related to a company.
-- **Example:** `/news/Tesla`
-
-### 2. Analyze News Sentiment
-**GET** `/analyze-news`
-- Performs sentiment analysis on the extracted articles.
-
-### 3. Compare News Articles
-**POST** `/compare-news`
-- Performs comparative analysis.
-- **Request Body:**
-```json
-{
-  "api_key": "your-openai-api-key",
-  "model_name": "gpt-4o-mini",
-  "company_name": "Tesla"
-}
-```
-
-### 4. Generate Hindi Summary 
-**GET** `/hindi-summary`
-- Returns the summarized analysis in Hindi and stores the speech file.
-
----
-
-## File Structure
-```
-├── api.py               # FastAPI backend for news extraction, sentiment analysis, and comparison
-├── app.py               # Gradio frontend to interact with users
-├── llm_utils.py         # Handles OpenAI API calls for topic extraction and comparative analysis
-├── utils.py             # Utility functions for web scraping, sentiment analysis, and TTS
-├── requirements.txt     # Dependencies
-└── README.md            # Project documentation
-```
-
----
-
-## Assumptions and Limitations
-- Only extracts articles from The New York Times and BBC.
-- Requires a valid OpenAI API key for sentiment analysis and comparison.
-- Hindi speech output uses gTTS, which requires an internet connection.
-
----
-
-## Deployment
-This project can be deployed on Hugging Face Spaces. To deploy:
-1. Push your repository to GitHub.
-2. Follow [Hugging Face Spaces documentation](https://huggingface.co/docs/spaces) for deployment.
-
----
-
-## Example Output
-```json
-{
-  "Company": "Tesla",
-  "Articles": [
-    {
-      "Title": "Tesla's New Model Breaks Sales Records",
-      "Summary": "Tesla's latest EV sees record sales in Q3...",
-      "Sentiment": "Positive",
-      "Topics": ["Electric Vehicles", "Stock Market", "Innovation"]
-    }
-  ],
-  "Comparative Sentiment Score": {
-    "Sentiment Distribution": {"Positive": 1, "Negative": 1, "Neutral": 0},
-    "Coverage Differences": [{
-      "Comparison": "Article 1 highlights Tesla's strong sales, while Article 2 discusses regulatory issues.",
-      "Impact": "Investors may react positively to growth news but stay cautious due to regulatory scrutiny."
-    }],
-    "Topic Overlap": {
-      "Common Topics": ["Electric Vehicles"],
-      "Unique Topics in Article 1": ["Stock Market", "Innovation"],
-      "Unique Topics in Article 2": ["Regulations", "Autonomous Vehicles"]
-    }
-  },
-  "Final Sentiment Analysis": "Tesla’s latest news coverage is mostly positive. Potential stock growth expected.",
-  "Audio": "[Play Hindi Speech]"
-}
-```
-
----
-
-## Contributing
-Feel free to contribute by:
-- Adding more news sources
-- Improving the sentiment model
-- Enhancing the UI
-
----
-
-## Contact
-For queries, reach out at [[email protected]].
-
+---
+title: News Summarizer
+emoji: 👁
+colorFrom: gray
+colorTo: green
+sdk: gradio
+sdk_version: 5.22.0
+app_file: api.py
+pinned: false
+short_description: An app for summarizing news articles on orgs.
+---
+
+# News Summarization and Text-to-Speech Application
+
+## Overview
+This project is a web-based application that extracts key details from multiple news articles related to a given company, performs sentiment analysis, conducts a comparative analysis, and generates a text-to-speech (TTS) output in Hindi.
+
+## Features
+- **News Extraction**: Scrapes and displays at least 10 news articles from The New York Times and BBC.
+- **Sentiment Analysis**: Categorizes articles into Positive, Negative, or Neutral sentiments.
+- **Comparative Analysis**: Groups articles with most semantic similarity. Then compares the groups to derive insights on how a company's news coverage varies.
+- **Text-to-Speech (TTS)**: Converts the summarized sentiment report into Hindi speech.
+- **User Interface**: Provides a simple web-based interface using Gradio.
+- **API Integration**: Implements FastAPI for backend communication.
+- **Deployment**: Deployable on Hugging Face Spaces.
+
+## Tech Stack
+- **Frontend**: Gradio
+- **Backend**: FastAPI
+- **Scraping**: BeautifulSoup
+- **NLP**: OpenAI GPT models, LangChain, Sentence Transformers
+- **Sentiment Analysis**: Pre-trained Transformer model
+- **Text-to-Speech**: Google TTS (gTTS)
+- **Deployment**: Uvicorn, Hugging Face Spaces
+
+---
+
+## Installation and Setup
+
+### 1. Clone the Repository
+```bash
+git clone https://github.com/Senzen18/News-Summarizer.git
+cd News-Summarizer
+```
+
+### 2. Install Dependencies
+Ensure you have Python 3.8+ installed. Then, run:
+```bash
+pip install -r requirements.txt
+```
+
+### 3. To run Fast API endpoints
+Start the FastAPI backend:
+```bash
+uvicorn api:app --host 127.0.0.1 --port 8000 --reload
+```
+
+### 4. To run the both Gradio and Fast API
+Start the FastAPI backend:
+```bash
+gradio app.py
+```
+
+### 5. Access the Application
+Once started, access the Gradio UI at:
+```
+http://127.0.0.1:7860
+```
+
+---
+
+## API Endpoints
+
+### 1. Fetch News
+**GET** `/news/{company_name}`
+- Fetches the latest articles related to a company.
+- **Example:** `/news/Tesla`
+
+### 2. Analyze News Sentiment
+**GET** `/analyze-news`
+- Performs sentiment analysis on the extracted articles.
+
+### 3. Compare News Articles
+**POST** `/compare-news`
+- Performs comparative analysis.
+- **Request Body:**
+```json
+{
+  "api_key": "your-openai-api-key",
+  "model_name": "gpt-4o-mini",
+  "company_name": "Tesla"
+}
+```
+
+### 4. Generate Hindi Summary 
+**GET** `/hindi-summary`
+- Returns the summarized analysis in Hindi and stores the speech file.
+
+---
+
+## File Structure
+```
+├── api.py               # FastAPI backend for news extraction, sentiment analysis, and comparison
+├── app.py               # Gradio frontend to interact with users
+├── llm_utils.py         # Handles OpenAI API calls for topic extraction and comparative analysis
+├── utils.py             # Utility functions for web scraping, sentiment analysis, and TTS
+├── requirements.txt     # Dependencies
+└── README.md            # Project documentation
+```
+
+---
+
+## Assumptions and Limitations
+- Only extracts articles from The New York Times and BBC.
+- Requires a valid OpenAI API key for sentiment analysis and comparison.
+- Hindi speech output uses gTTS, which requires an internet connection.
+
+---
+
+## Deployment
+This project can be deployed on Hugging Face Spaces. To deploy:
+1. Push your repository to GitHub.
+2. Follow [Hugging Face Spaces documentation](https://huggingface.co/docs/spaces) for deployment.
+
+---
+
+## Example Output
+```json
+{
+  "Company": "Tesla",
+  "Articles": [
+    {
+      "Title": "Tesla's New Model Breaks Sales Records",
+      "Summary": "Tesla's latest EV sees record sales in Q3...",
+      "Sentiment": "Positive",
+      "Topics": ["Electric Vehicles", "Stock Market", "Innovation"]
+    }
+  ],
+  "Comparative Sentiment Score": {
+    "Sentiment Distribution": {"Positive": 1, "Negative": 1, "Neutral": 0},
+    "Coverage Differences": [{
+      "Comparison": "Article 1 highlights Tesla's strong sales, while Article 2 discusses regulatory issues.",
+      "Impact": "Investors may react positively to growth news but stay cautious due to regulatory scrutiny."
+    }],
+    "Topic Overlap": {
+      "Common Topics": ["Electric Vehicles"],
+      "Unique Topics in Article 1": ["Stock Market", "Innovation"],
+      "Unique Topics in Article 2": ["Regulations", "Autonomous Vehicles"]
+    }
+  },
+  "Final Sentiment Analysis": "Tesla’s latest news coverage is mostly positive. Potential stock growth expected.",
+  "Audio": "[Play Hindi Speech]"
+}
+```
+
+---
+
+## Contributing
+Feel free to contribute by:
+- Adding more news sources
+- Improving the sentiment model
+- Enhancing the UI
+
+---
+
+## Contact
+For queries, reach out at [[email protected]].
+