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README.md

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 ---
-title: AgriScholarQA
-emoji: 👁
-colorFrom: purple
-colorTo: gray
-sdk: docker
+title: Agri-Critique Research Assistant
+emoji: 🌾
+colorFrom: green
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.28.0
+app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Agri-Critique: Self-Correcting Agricultural QA System
+
+## Overview
+
+**Agri-Critique** is an evidence-based question-answering system for agricultural research, powered by a fine-tuned Llama 3.1 8B model trained on 2,859 QA pairs with self-correction capabilities.
+
+### Key Features
+
+- 🎯 **Evidence-Grounded Answers**: All responses cite scientific papers
+- 🔍 **Hallucination Detection**: Trained to identify and correct errors
+- 🔎 **Critique Mode**: Validate any answer for accuracy and errors
+- 💬 **Conversational**: Maintains context across multiple questions
+- 📚 **Citation-Aware**: Provides paper references for verification
+
+### Two Interaction Modes
+
+1. **💬 Q&A Mode**: Ask questions and get evidence-based answers
+2. **🔍 Critique Mode**: Submit a question + proposed answer to validate for:
+   - Hallucinations (fake findings)
+   - Unsupported claims
+   - Temporal/causal errors
+   - Missing citations
+
+### Model Details
+
+- **Base Model**: Llama 3.1 8B Instruct
+- **Fine-tuning**: LoRA (rank=32, alpha=64)
+- **Training Data**: 
+  - 1,814 standard QA pairs (63.4%)
+  - 1,045 adversarial critique pairs (36.6%)
+- **Training Objective**: Mixed curriculum (answer + critique)
+
+### Dataset Composition
+
+The model was trained on **Agri-Critique**, a novel dataset combining:
+
+1. **Standard QA**: Evidence-based agricultural questions
+2. **Adversarial QA**: Intentionally flawed answers with critiques
+   - Hallucinations (fake findings)
+   - Temporal mismatches
+   - Causal reversals
+   - Unsupported extrapolations
+
+### Research Contribution
+
+This system demonstrates:
+- **Self-Correction**: Models can learn to detect their own errors
+- **Citation Integrity**: Reduces "fake citation" problem in RAG systems
+- **Domain Adaptation**: Specialized for agricultural/scientific QA
+
+### Usage
+
+1. Type your agricultural research question
+2. View the evidence-based answer with citations
+3. Expand "View Evidence Sources" to see paper excerpts
+4. Ask follow-up questions for multi-turn conversation
+
+### Example Questions
+
+- "What is the effect of nitrogen fertilizer on rice yield?"
+- "How does drought stress affect wheat production?"
+- "What are the benefits of crop rotation?"
+- "Explain the impact of climate change on agriculture."
+
+### Limitations
+
+- **Domain-Specific**: Optimized for agricultural/scientific questions
+- **Evidence-Dependent**: Answers limited to indexed papers
+- **Not Hallucination-Free**: While reduced, some errors may occur
+- **English Only**: Currently supports English language only
+
+### Citation
+
+If you use this system in your research, please cite:
+
+```bibtex
+@article{agri-critique-2025,
+  title={Agri-Critique: Training Self-Correcting Language Models for Agricultural Question Answering},
+  author={Your Name},
+  journal={arXiv preprint},
+  year={2025}
+}
+```
+
+### Links
+
+- 📄 [Paper](https://arxiv.org/your-paper) (Coming soon)
+- 💻 [GitHub](https://github.com/your-repo)
+- 🤗 [Model](https://huggingface.co/your-username/agri-critique-llama)
+- 📊 [Dataset](https://huggingface.co/datasets/your-username/agri-critique)
+
+### License
+
+MIT License - See LICENSE file for details
+
+### Acknowledgments
+
+Built with:
+- Llama 3.1 by Meta
+- Hugging Face Transformers
+- FAISS for vector search
+- Streamlit for UI