added config in readme

README.md

@@ -1,102 +1,114 @@
-### **Implementation Steps: Validating Information with Context**  
-
-Validating the accuracy or degree of truthfulness of a given piece of information requires **context**—factual and relevant details surrounding the claim. Here’s how we approach this process step-by-step:
-
----
-
-### **Step 1: Retrieving Context from Knowledge Graph Substitute - FAISS with Semantic Search**  
-Instead of relying on a **traditional Knowledge Graph (KG)**, we use **FAISS (Facebook AI Similarity Search)**, a **faster, scalable, and flexible alternative** for semantic search.  
-
-#### **Why FAISS is Better than a Traditional KG**  
-1. **Sentence-Level Retrieval**: Unlike traditional KGs that often rely on pre-defined **entities and relationships**, FAISS uses dense **embeddings** to directly match the **semantic meaning** of entire sentences.  
-2. **Scalable and High-Speed Retrieval**: FAISS efficiently handles **millions of embeddings**, making it highly scalable for real-world applications.  
-3. **Flexibility**: It works with **unstructured text**, removing the need to pre-process information into entities and relations, which is often time-consuming.  
-4. **Generalization**: FAISS enables **approximate nearest neighbor (ANN) search**, allowing retrieval of contextually related results, even if they are not exact matches.
-
-#### **Dataset Used**  
-We leverage the **News Category Dataset** ([Kaggle Link](https://www.kaggle.com/datasets/rmisra/news-category-dataset)), which contains **news headlines and short descriptions** across various categories.  
-
-- **Why This Dataset?**  
-  It covers a **wide range of topics**, making it useful for general-purpose context building.  
-  - Headlines and descriptions provide **rich semantic embeddings** for similarity searches.  
-  - Categories allow filtering relevant results if required (e.g., "science" or "technology").
-
-**Process:**
-1. We use **SentenceTransformer (all-MiniLM-L6-v2)** to generate embeddings for the query (the input news).  
-2. We search against pre-computed embeddings stored in a **FAISS index** to retrieve the **top-K most relevant entries**.  
-3. These results form the **initial context**, capturing related information already present in the dataset.
-
----
-
-### **Step 2: Online Search for Real-Time Context**  
-To **augment** the context retrieved from FAISS, we incorporate **real-time online search** using an API.  
-
-#### **Why Online Search is Critical?**  
-- **Fresh Information**: News and facts evolve, especially in areas like **science, technology, or politics**. Online search ensures access to the **latest updates** that may not exist in the static dataset.  
-- **Diverse Sources**: It broadens the scope by pulling information from **multiple credible sources**, reducing bias and enhancing reliability.  
-- **Fact-Checking**: Search engines often index **trusted fact-checking websites** that we can incorporate into the context.
-
-**Process:**
-1. Use an API with a **search query** derived from the input news.  
-2. Retrieve relevant snippets, headlines, or summaries.  
-3. Append these results to the **context** built using FAISS.
-
----
-
-### **Step 3: Building Context from Combined Sources**  
-Both FAISS-based retrieval and **online search results** are combined into a **single context string**. This provides a **comprehensive knowledge base** around the input information.  
-
-- **Why Combine Both?**  
-  - FAISS offers **pre-indexed knowledge**—ideal for **static facts** or concepts.  
-  - Online search complements it with **dynamic and up-to-date insights**—perfect for verifying **recent developments**.  
-
-This layered context improves the model’s ability to assess the **truthfulness** of the given information.
-
----
-
-### **Step 4: Truthfulness Prediction with Zero-Shot Classification Model**  
-We use the **Facebook/BART-Large-MNLI** model, a **zero-shot classification** model, for evaluation.  
-
-#### **Why BART-Large-MNLI?**  
-1. **Zero-Shot Capability**: It can handle claims and hypotheses without needing **task-specific training**—perfect for this flexible, multi-domain use case.  
-2. **Contextual Matching**: It compares the input claim (news) with the constructed context to assess **semantic consistency**.  
-3. **High Accuracy**: Pre-trained on **natural language inference tasks**, making it adept at understanding relationships like **entailment** and **contradiction**.  
-4. **Multi-Label Support**: Can evaluate multiple labels simultaneously, ideal for **degrees of truthfulness**.
-
-**Process:**
-1. Input the **news** as the claim and the **context** as the hypothesis.  
-2. Compute a **truthfulness score** between **0 and 1**, where:  
-   - **0**: Completely **false**.  
-   - **1**: Completely **true**.  
-3. Generate **explanations** based on the score and suggest actions (e.g., further verification if uncertain).
-
----
-
-### **End-to-End Example**  
-**Input News:**  
-"Scientists Demonstrate 'Negative Time' In Groundbreaking Quantum Experiment."  
-
-**Context Built:**  
-- **FAISS Search:** Finds prior research on **quantum time reversal** and **entanglement theories**.  
-- **Online Search:** Retrieves recent articles discussing **quantum breakthroughs** and expert views.  
-
-**Model Evaluation:**  
-- Model compares the news with the combined context and outputs:  
-  **Score: 0.72** (Likely True).  
-
-**Result Explanation:**  
-```plaintext
-News: "Scientists Demonstrate 'Negative Time' In Groundbreaking Quantum Experiment."
-Truthfulness Score: 0.72 (Likely true)
-Analysis: You can reasonably trust this information, but further verification is always recommended for critical decisions.
-```
-
----
-
-### **Why This Approach Works?**  
-1. **Balanced Context**: Combines static knowledge (KG substitute) and dynamic knowledge (real-time search).  
-2. **Model Flexibility**: Zero-shot model adapts to diverse topics without retraining.  
-3. **Scalable and Cost-Effective**: Uses pre-trained models, FAISS indexing, and simple APIs for implementation.  
-4. **Interpretability**: Outputs include confidence scores and explanations for transparency.
-
+---
+title: Truthfulness Checker
+emoji: 📰
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 5.4.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+### **Implementation Steps: Validating Information with Context**  
+
+Validating the accuracy or degree of truthfulness of a given piece of information requires **context**—factual and relevant details surrounding the claim. Here’s how we approach this process step-by-step:
+
+---
+
+### **Step 1: Retrieving Context from Knowledge Graph Substitute - FAISS with Semantic Search**  
+Instead of relying on a **traditional Knowledge Graph (KG)**, we use **FAISS (Facebook AI Similarity Search)**, a **faster, scalable, and flexible alternative** for semantic search.  
+
+#### **Why FAISS is Better than a Traditional KG**  
+1. **Sentence-Level Retrieval**: Unlike traditional KGs that often rely on pre-defined **entities and relationships**, FAISS uses dense **embeddings** to directly match the **semantic meaning** of entire sentences.  
+2. **Scalable and High-Speed Retrieval**: FAISS efficiently handles **millions of embeddings**, making it highly scalable for real-world applications.  
+3. **Flexibility**: It works with **unstructured text**, removing the need to pre-process information into entities and relations, which is often time-consuming.  
+4. **Generalization**: FAISS enables **approximate nearest neighbor (ANN) search**, allowing retrieval of contextually related results, even if they are not exact matches.
+
+#### **Dataset Used**  
+We leverage the **News Category Dataset** ([Kaggle Link](https://www.kaggle.com/datasets/rmisra/news-category-dataset)), which contains **news headlines and short descriptions** across various categories.  
+
+- **Why This Dataset?**  
+  It covers a **wide range of topics**, making it useful for general-purpose context building.  
+  - Headlines and descriptions provide **rich semantic embeddings** for similarity searches.  
+  - Categories allow filtering relevant results if required (e.g., "science" or "technology").
+
+**Process:**
+1. We use **SentenceTransformer (all-MiniLM-L6-v2)** to generate embeddings for the query (the input news).  
+2. We search against pre-computed embeddings stored in a **FAISS index** to retrieve the **top-K most relevant entries**.  
+3. These results form the **initial context**, capturing related information already present in the dataset.
+
+---
+
+### **Step 2: Online Search for Real-Time Context**  
+To **augment** the context retrieved from FAISS, we incorporate **real-time online search** using an API.  
+
+#### **Why Online Search is Critical?**  
+- **Fresh Information**: News and facts evolve, especially in areas like **science, technology, or politics**. Online search ensures access to the **latest updates** that may not exist in the static dataset.  
+- **Diverse Sources**: It broadens the scope by pulling information from **multiple credible sources**, reducing bias and enhancing reliability.  
+- **Fact-Checking**: Search engines often index **trusted fact-checking websites** that we can incorporate into the context.
+
+**Process:**
+1. Use an API with a **search query** derived from the input news.  
+2. Retrieve relevant snippets, headlines, or summaries.  
+3. Append these results to the **context** built using FAISS.
+
+---
+
+### **Step 3: Building Context from Combined Sources**  
+Both FAISS-based retrieval and **online search results** are combined into a **single context string**. This provides a **comprehensive knowledge base** around the input information.  
+
+- **Why Combine Both?**  
+  - FAISS offers **pre-indexed knowledge**—ideal for **static facts** or concepts.  
+  - Online search complements it with **dynamic and up-to-date insights**—perfect for verifying **recent developments**.  
+
+This layered context improves the model’s ability to assess the **truthfulness** of the given information.
+
+---
+
+### **Step 4: Truthfulness Prediction with Zero-Shot Classification Model**  
+We use the **Facebook/BART-Large-MNLI** model, a **zero-shot classification** model, for evaluation.  
+
+#### **Why BART-Large-MNLI?**  
+1. **Zero-Shot Capability**: It can handle claims and hypotheses without needing **task-specific training**—perfect for this flexible, multi-domain use case.  
+2. **Contextual Matching**: It compares the input claim (news) with the constructed context to assess **semantic consistency**.  
+3. **High Accuracy**: Pre-trained on **natural language inference tasks**, making it adept at understanding relationships like **entailment** and **contradiction**.  
+4. **Multi-Label Support**: Can evaluate multiple labels simultaneously, ideal for **degrees of truthfulness**.
+
+**Process:**
+1. Input the **news** as the claim and the **context** as the hypothesis.  
+2. Compute a **truthfulness score** between **0 and 1**, where:  
+   - **0**: Completely **false**.  
+   - **1**: Completely **true**.  
+3. Generate **explanations** based on the score and suggest actions (e.g., further verification if uncertain).
+
+---
+
+### **End-to-End Example**  
+**Input News:**  
+"Scientists Demonstrate 'Negative Time' In Groundbreaking Quantum Experiment."  
+
+**Context Built:**  
+- **FAISS Search:** Finds prior research on **quantum time reversal** and **entanglement theories**.  
+- **Online Search:** Retrieves recent articles discussing **quantum breakthroughs** and expert views.  
+
+**Model Evaluation:**  
+- Model compares the news with the combined context and outputs:  
+  **Score: 0.72** (Likely True).  
+
+**Result Explanation:**  
+```plaintext
+News: "Scientists Demonstrate 'Negative Time' In Groundbreaking Quantum Experiment."
+Truthfulness Score: 0.72 (Likely true)
+Analysis: You can reasonably trust this information, but further verification is always recommended for critical decisions.
+```
+
+---
+
+### **Why This Approach Works?**  
+1. **Balanced Context**: Combines static knowledge (KG substitute) and dynamic knowledge (real-time search).  
+2. **Model Flexibility**: Zero-shot model adapts to diverse topics without retraining.  
+3. **Scalable and Cost-Effective**: Uses pre-trained models, FAISS indexing, and simple APIs for implementation.  
+4. **Interpretability**: Outputs include confidence scores and explanations for transparency.
+
 This modular approach ensures that the **truthfulness assessment** is **scalable**, **explainable**, and **adaptable** to new domains.