--- a/README.md
+++ b/README.md
@@ -1,226 +1,224 @@
-# AItrika
-
-
-
-[](https://opensource.org/licenses/Apache-2.0)
-
-
-
-
-
-
-Enhance your knowledge in medical research.
-
-AItrika (formerly **PubGPT**) is a tool that can extract lots of relevant informations inside medical papers in an easy way:
-
-- Abstract
-- Full text (when available)
-- Genes
-- Diseases
-- Mutations
-- Associations between genes and diseases
-- MeSH terms
-- Other terms
-- Results
-- Bibliography
-
-And so on!
-
-## 🚀 Run the demo app
-
-You can try AItrika with the Streamlit app by running:
-
-```
-streamlit run app.py
-```
-
-Or you can use it a script by running:
-
-```
-python main.py
-```
-
-## 📦 Install
-
-To install everything, you need `uv`.
-
-First of all, install `uv` with the command:
-
-```
-python main.py
-```
-
-After that, create a virtual environment with the command:
-
-```
-uv venv venv_name
-```
-
-Activate the virtual env:
-
-```
-source venv_name/bin/activate
-```
-
-And install dependencies:
-
-```
-uv pip install -r requirements.in
-```
-
-## 🔑 Set LLM API Keys
-
-In order to set API keys, insert your keys into the `env.example` file and rename it to `.env`.
-
-## 🔍 Usage
-
-You can easily get informations of a paper by passing a PubMed ID:
-
-```python
-from aitrika.engine.aitrika import OnlineAItrika
-aitrika_engine = OnlineAItrika(pubmed_id=pubmed_id)
-title = aitrika_engine.get_title()
-print(title)
-```
-
-Or you can parse a local pdf:
-
-```python
-from aitrika.engine.aitrika import LocalAItrika
-aitrika_engine = LocalAItrika(pdf_path = pdf_path)
-title = aitrika_engine.get_title()
-print(title)
-```
-
-```
-Breast cancer genes: beyond BRCA1 and BRCA2.
-```
-
-You can get other informations, like the associations between genes and diseases:
-
-```python
-associations = aitrika_engine.get_associations()
-```
-
-```
-[
- {
- "gene": "BRIP1",
- "disease": "Breast Neoplasms"
- },
- {
- "gene": "PTEN",
- "disease": "Breast Neoplasms"
- },
- {
- "gene": "CHEK2",
- "disease": "Breast Neoplasms"
- },
-]
-...
-```
-
-Or you can get a nice formatted DataFrame:
-
-```python
-associations = aitrika_engine.associations(dataframe = True)
-```
-
-```
- gene disease
-0 BRIP1 Breast Neoplasms
-1 PTEN Breast Neoplasms
-2 CHEK2 Breast Neoplasms
-...
-```
-
-With the power of RAG, you can query your document:
-
-```python
-## Prepare the documents
-documents = generate_documents(content=abstract)
-
-## Set the LLM
-llm = GroqLLM(documents=documents, api_key=os.getenv("GROQ_API_KEY"))
-
-## Query your document
-query = "Is BRCA1 associated with breast cancer?"
-print(llm.query(query=query))
-```
-
-```
-The provided text suggests that BRCA1 is associated with breast cancer, as it is listed among the high-penetrance genes identified in family linkage studies as responsible for inherited syndromes of breast cancer.
-```
-
-Or you can extract other informations:
-
-```python
-results = engine.extract_results(llm=llm)
-print(results)
-```
-
-```
-** RESULTS **
-
-- High-penetrance genes - BRCA1, BRCA2, PTEN, TP53 - responsible for inherited syndromes
-- Moderate-penetrance genes - CHEK2, ATM, BRIP1, PALB2, RAD51C - associated with moderate BC risk
-- Low-penetrance alleles - common alleles - associated with slightly increased or decreased risk of BC
-- Current clinical practice - high-penetrance genes - widely used
-- Future prospect - all familial breast cancer genes - to be included in genetic test
-- Research need - clinical management - of moderate and low-risk variants
-```
-
-## 🚀 Run the API
-
-To run the AItrika API, follow these steps:
-
-1. Ensure you have set up your environment and installed all dependencies as described in the Installation section.
-
-2. Run the API server using the following command:
-
-```bash
-python api.py
-```
-
-The API will start running on http://0.0.0.0:8000. You can now make requests to the various endpoints:
-
-- /associations: Get associations from a PubMed article
-- /abstract: Get abstract of a PubMed article
-- /query: Query a PubMed article
-- /results: Get results from a PubMed article
-- /participants: Get number of participants from a PubMed article
-- /outcomes: Get outcomes from a PubMed article
-
-You can use tools like curl, Postman, or any HTTP client to interact with the API. For example:
-
-```bash
-curl -X POST "http://localhost:8000/abstract" -H "Content-Type: application/json" -d '{"pubmed_id": 12345678}'
-```
-
-The API documentation is automatically generated and saved to <code>docs/api-reference/openapi.json</code>.
-You can use this file with tools like Swagger UI for a more interactive API exploration experience.
-
-## Support the Project
-
-If you find this project useful, please consider supporting it:
-
-- 🌟 Star the project on GitHub
-- 🐛 Report bugs or suggest new features
-- 🤝 Contribute with pull requests
-- ☕️ [Buy me a coffee](https://www.buymeacoffee.com/dsupertramp) or consider a sponsor.
-
-### Commercial / Business use
-
-If you're using this project in a business or commercial context, please [contact me](salvatoredanilopalumbo@gmail.com).
-
-I'm available for consulting, custom development, or commercial licensing.
-
-Your support helps keep this project active and continuously improving. Thank you!
-
-## License
-
-AItrika is licensed under the Apache 2.0 License. See the LICENSE file for more details.
-
-## Star History
-
-[](https://star-history.com/#dSupertramp/AItrika&Date)
+# AItrika
+
+[](https://opensource.org/licenses/Apache-2.0)
+
+
+
+
+
+
+Enhance your knowledge in medical research.
+
+AItrika (formerly **PubGPT**) is a tool that can extract lots of relevant informations inside medical papers in an easy way:
+
+- Abstract
+- Full text (when available)
+- Genes
+- Diseases
+- Mutations
+- Associations between genes and diseases
+- MeSH terms
+- Other terms
+- Results
+- Bibliography
+
+And so on!
+
+## 🚀 Run the demo app
+
+You can try AItrika with the Streamlit app by running:
+
+```
+streamlit run app.py
+```
+
+Or you can use it a script by running:
+
+```
+python main.py
+```
+
+## 📦 Install
+
+To install everything, you need `uv`.
+
+First of all, install `uv` with the command:
+
+```
+python main.py
+```
+
+After that, create a virtual environment with the command:
+
+```
+uv venv venv_name
+```
+
+Activate the virtual env:
+
+```
+source venv_name/bin/activate
+```
+
+And install dependencies:
+
+```
+uv pip install -r requirements.in
+```
+
+## 🔑 Set LLM API Keys
+
+In order to set API keys, insert your keys into the `env.example` file and rename it to `.env`.
+
+## 🔍 Usage
+
+You can easily get informations of a paper by passing a PubMed ID:
+
+```python
+from aitrika.engine.aitrika import OnlineAItrika
+aitrika_engine = OnlineAItrika(pubmed_id=pubmed_id)
+title = aitrika_engine.get_title()
+print(title)
+```
+
+Or you can parse a local pdf:
+
+```python
+from aitrika.engine.aitrika import LocalAItrika
+aitrika_engine = LocalAItrika(pdf_path = pdf_path)
+title = aitrika_engine.get_title()
+print(title)
+```
+
+```
+Breast cancer genes: beyond BRCA1 and BRCA2.
+```
+
+You can get other informations, like the associations between genes and diseases:
+
+```python
+associations = aitrika_engine.get_associations()
+```
+
+```
+[
+ {
+ "gene": "BRIP1",
+ "disease": "Breast Neoplasms"
+ },
+ {
+ "gene": "PTEN",
+ "disease": "Breast Neoplasms"
+ },
+ {
+ "gene": "CHEK2",
+ "disease": "Breast Neoplasms"
+ },
+]
+...
+```
+
+Or you can get a nice formatted DataFrame:
+
+```python
+associations = aitrika_engine.associations(dataframe = True)
+```
+
+```
+ gene disease
+0 BRIP1 Breast Neoplasms
+1 PTEN Breast Neoplasms
+2 CHEK2 Breast Neoplasms
+...
+```
+
+With the power of RAG, you can query your document:
+
+```python
+## Prepare the documents
+documents = generate_documents(content=abstract)
+
+## Set the LLM
+llm = GroqLLM(documents=documents, api_key=os.getenv("GROQ_API_KEY"))
+
+## Query your document
+query = "Is BRCA1 associated with breast cancer?"
+print(llm.query(query=query))
+```
+
+```
+The provided text suggests that BRCA1 is associated with breast cancer, as it is listed among the high-penetrance genes identified in family linkage studies as responsible for inherited syndromes of breast cancer.
+```
+
+Or you can extract other informations:
+
+```python
+results = engine.extract_results(llm=llm)
+print(results)
+```
+
+```
+** RESULTS **
+
+- High-penetrance genes - BRCA1, BRCA2, PTEN, TP53 - responsible for inherited syndromes
+- Moderate-penetrance genes - CHEK2, ATM, BRIP1, PALB2, RAD51C - associated with moderate BC risk
+- Low-penetrance alleles - common alleles - associated with slightly increased or decreased risk of BC
+- Current clinical practice - high-penetrance genes - widely used
+- Future prospect - all familial breast cancer genes - to be included in genetic test
+- Research need - clinical management - of moderate and low-risk variants
+```
+
+## 🚀 Run the API
+
+To run the AItrika API, follow these steps:
+
+1. Ensure you have set up your environment and installed all dependencies as described in the Installation section.
+
+2. Run the API server using the following command:
+
+```bash
+python api.py
+```
+
+The API will start running on http://0.0.0.0:8000. You can now make requests to the various endpoints:
+
+- /associations: Get associations from a PubMed article
+- /abstract: Get abstract of a PubMed article
+- /query: Query a PubMed article
+- /results: Get results from a PubMed article
+- /participants: Get number of participants from a PubMed article
+- /outcomes: Get outcomes from a PubMed article
+
+You can use tools like curl, Postman, or any HTTP client to interact with the API. For example:
+
+```bash
+curl -X POST "http://localhost:8000/abstract" -H "Content-Type: application/json" -d '{"pubmed_id": 12345678}'
+```
+
+The API documentation is automatically generated and saved to <code>docs/api-reference/openapi.json</code>.
+You can use this file with tools like Swagger UI for a more interactive API exploration experience.
+
+## Support the Project
+
+If you find this project useful, please consider supporting it:
+
+- 🌟 Star the project on GitHub
+- 🐛 Report bugs or suggest new features
+- 🤝 Contribute with pull requests
+- ☕️ [Buy me a coffee](https://www.buymeacoffee.com/dsupertramp) or consider a sponsor.
+
+### Commercial / Business use
+
+If you're using this project in a business or commercial context, please [contact me](salvatoredanilopalumbo@gmail.com).
+
+I'm available for consulting, custom development, or commercial licensing.
+
+Your support helps keep this project active and continuously improving. Thank you!
+
+## License
+
+AItrika is licensed under the Apache 2.0 License. See the LICENSE file for more details.
+
+## Star History
+
+[](https://star-history.com/#dSupertramp/AItrika&Date)
Datasets
Models
