Add Hugging Face Space metadata to README.md

README.md

@@ -1,198 +1,12 @@
-# 🔬 ResearchMind AI
-
-<div align="center">
-
-![Python](https://img.shields.io/badge/Python-3.10+-3776AB?style=for-the-badge&logo=python&logoColor=white)
-![Gradio](https://img.shields.io/badge/Gradio-UI-FF6F00?style=for-the-badge)
-![Groq](https://img.shields.io/badge/Groq-LLM-000000?style=for-the-badge)
-![RAG](https://img.shields.io/badge/RAG-Retrieval--Augmented--Generation-6A5ACD?style=for-the-badge)
-![Agentic](https://img.shields.io/badge/Agentic-Plan%20%E2%86%92%20Write%20%E2%86%92%20Verify-E91E63?style=for-the-badge)
-![FAISS](https://img.shields.io/badge/FAISS-Vector%20Search-009688?style=for-the-badge)
-![Tests](https://img.shields.io/badge/Tests-26%20passing-2E7D32?style=for-the-badge)
-![License](https://img.shields.io/badge/License-MIT-green?style=for-the-badge)
-
-### A multi-agent research assistant that plans, researches, writes, and **fact-checks its own reports**
-
-</div>
-
----
-
-## 📖 Overview
-
-Most AI research tools return fluent text you have to trust blindly. ResearchMind AI is built around a different idea: **every claim in the report is cited, and a second agent checks each claim against the exact passages it cites** before you see the result.
-
-Give it a topic and a team of cooperating agents does the work:
-
-| Agent | Job |
-|---|---|
-| 🧭 **Planner** | Breaks the topic into focused research sub-questions |
-| 🔍 **Researcher** | Searches the web for every sub-question in parallel (Tavily) |
-| 🧩 **Retriever** | Chunks and embeds all sources, then ranks the best passages with FAISS cosine search |
-| ✍️ **Writer** | Drafts a report that may only use the retrieved sources, citing them as `[1]`, `[2]` |
-| 🔎 **Checker** | Verifies each cited claim against the passages it cites: *supported / partial / unsupported* |
-| 🛠️ **Reviser** | Rewrites or removes flagged claims, then the Checker re-verifies (the feedback loop) |
-| 🌍 **Translator** | Optionally renders the *verified* English report in Urdu |
-
-The UI shows this activity live: the plan, sources found, passages retrieved, and the fact-check verdicts.
-
----
-
-## ✨ Features
-
-- **Agentic pipeline** with a self-correcting verify → revise loop
-- **Real RAG**: per-request in-memory FAISS index (normalized embeddings, cosine similarity), per-source caps so one page cannot dominate, relevance floor to drop weak passages
-- **Inline citations** `[n]` with a sources list built by code (URLs are never invented by the model); invented citation numbers are stripped automatically
-- **Claim-level verification** with an honest score shown on every report
-- **Bring your own documents**: upload PDF / TXT / MD, mix them with web results, or run on your documents only
-- **Research depth** presets: Quick, Standard, Deep
-- **Report styles**: Standard, Summary, Academic
-- **English and Urdu** output (Urdu is translated *after* verification, so the checking happens on the original English text)
-- **Export** to Markdown (all languages) and PDF (English) with clickable source links
-- **Model fallback and rate-limit retry** across several Groq models
-- **Offline test suite** (26 tests) and a **batch evaluation script**
-
----
-
-## 🏗️ Architecture
-
-```text
-                    Topic + options (style, depth, language, documents)
-                                      │
-                                      ▼
-                         ┌────────────────────────┐
-                         │  Planner (fast LLM)    │──► 2–5 sub-questions
-                         └───────────┬────────────┘
-                                     ▼
-                  ┌─────────────────────────────────────┐
-                  │ Researcher: parallel Tavily search  │  + uploaded PDF/TXT/MD
-                  └───────────────────┬─────────────────┘
-                                      ▼
-                  ┌─────────────────────────────────────┐
-                  │ Retriever: chunk → embed (MiniLM)   │
-                  │ → FAISS cosine index → top passages │
-                  │   per sub-question                  │
-                  └───────────────────┬─────────────────┘
-                                      ▼
-                         ┌────────────────────────┐
-                         │ Writer (70B, streaming)│──► cited draft
-                         └───────────┬────────────┘
-                                     ▼
-              ┌─────────────────────────────────────────────┐
-              │ Checker: each claim vs. the passages it cites│
-              └───────────┬─────────────────────────────────┘
-                          │ flagged claims?
-                  yes ┌───┴───┐ no
-                      ▼       │
-              ┌──────────────┐│
-              │   Reviser    ││   (re-check; keep the revision only if it scores better)
-              └──────┬───────┘│
-                     └────────┤
-                              ▼
-                  Translator (optional) → Report + Sources + Verification
-```
-
----
-
-## 🧰 Tech Stack
-
-Python · Gradio · Groq (Llama 3.3 70B for writing, Llama 3.1 8B for planning and checking, with fallbacks) · Tavily · Sentence-Transformers (`all-MiniLM-L6-v2`) · FAISS · LangChain text splitters · pypdf · ReportLab
-
----
-
-## 🚀 Getting Started
-
-```bash
-git clone https://github.com/muhammadazfar-ai/ResearchMind-AI.git
-cd ResearchMind-AI
-python -m venv .venv && source .venv/bin/activate      # Windows: .venv\Scripts\activate
-pip install -r requirements.txt
-cp .env.example .env                                    # then add your keys
-python app.py
-```
-
-You need a free [Groq](https://console.groq.com) key and a [Tavily](https://tavily.com) key. Never commit `.env`.
-
-### Run on Hugging Face Spaces
-1. Create a new **Gradio** Space and push this repository.
-2. In *Settings → Variables and secrets*, add `GROQ_API_KEY` and `TAVILY_API_KEY` as **secrets**.
-3. The Space installs `requirements.txt` and runs `app.py` automatically.
-
----
-
-## 📊 Evaluation
-
-Run the pipeline over a topic list and measure how well-grounded the reports are:
-
-```bash
-python evaluate.py --limit 5            # first 5 topics in evaluation/topics.txt
-python evaluate.py --depth Quick --pause 15
-```
-
-It writes `evaluation/results.json` and `evaluation/RESULTS.md` with, per topic and on average:
-- **Claim support rate**: share of cited claims fully supported by their sources, for the first draft and after the Checker/Reviser loop
-- **Citation coverage**: share of factual sentences that carry a citation
-- Sources per report, claims checked, and time per report
-
-> Paste your own `RESULTS.md` summary here after running it, so the numbers in this README are yours.
-
 ---
-
-## 🧪 Tests
-
-```bash
-pip install -r requirements-dev.txt
-python -m pytest
-```
-
-The suite runs fully offline with scripted fake search, embedding and LLM components. It covers retrieval ranking, citation cleaning, claim extraction, the full verify → revise loop (including a planted false claim that must be caught and removed), document loading, Urdu translation flow, error paths, and the UI handler.
-
+title: ResearchMind AI
+emoji: ??
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+app_file: app.py
+pinned: false
 ---
 
-## ⚙️ Configuration
-
-All tuning lives in [`src/config.py`](src/config.py): chunk size, depth presets, how many passages go to the Writer, relevance thresholds, fact-check batch size. Model order per role is in [`src/llm.py`](src/llm.py) and can be overridden with `GROQ_WRITER_MODEL` / `GROQ_FAST_MODEL`.
-
----
-
-## ⚠️ Limitations
-
-- **The fact-check measures grounding, not absolute truth.** An LLM judges whether a claim matches the passages it cites. If a source is wrong, the report can still be "supported". Verify critical facts yourself.
-- **Free-tier rate limits.** Deep research makes many API calls; the app retries and falls back to other models, but it can still be slow or fail under tight limits.
-- **Urdu PDF export is not supported** (standard PDF fonts cannot shape Urdu). Urdu reports download as Markdown.
-- **Scanned PDFs** have no text layer and need OCR before upload.
-- The Urdu translation is machine-generated and was not reviewed by a native speaker.
-
----
-
-## 📂 Project Structure
-
-```text
-ResearchMind-AI/
-├── app.py                 # Gradio UI: controls, live agent log, exports
-├── evaluate.py            # Batch evaluation (support rate, coverage, timing)
-├── requirements.txt
-├── requirements-dev.txt
-├── pytest.ini
-├── .env.example
-├── evaluation/
-│   └── topics.txt         # Topics used by evaluate.py
-├── src/
-│   ├── agents.py          # Planner, Researcher, Writer, Checker, Reviser, Translator
-│   ├── pipeline.py        # Orchestration + live activity trace
-│   ├── evidence.py        # Chunking, FAISS index, passage selection per question
-│   ├── embeddings.py      # Sentence-Transformers + cosine index
-│   ├── search.py          # Tavily web search
-│   ├── documents.py       # PDF / TXT / MD loading
-│   ├── llm.py             # Groq client, model fallback, streaming
-│   ├── prompts.py         # All agent prompts
-│   ├── chunking.py        # Recursive text splitter
-│   └── config.py          # Tunable settings
-├── tests/                 # Offline test suite
-└── screenshot/
-```
-
----
-
-## 📄 License
-
-MIT. See [LICENSE](LICENSE).
+# ResearchMind AI
+A multi-agent AI research assistant.