Readme and Disclaimer update
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README.md
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Gina Bot is an AI-powered conversational assistant designed to help you understand compliance with and analyze the Circular Economy topic. This tool leverages advanced language models to help you get clear and structured answers about Circular Economy requirements, compliance procedures, and regulatory guidance.
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It combines a generative language assistant with a knowledge base implemented via Retrieval-Augmented Generation (RAG). The scope and functionality of the tool is focused on
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## 2. Models Used
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- The user interface clearly indicates the use of a generative AI model.
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- An explanation of the RAG method is included.
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- We collect usage statistics as detailed in Disclaimer tab of the app along with the explicit display in the user interface of the tool.
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- Feedback mechanism available (via https://huggingface.co/spaces/GIZ/
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## 7. Monitoring, Feedback, and Incident Reporting
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- User can provide feedback via UI by giving (Thumbs-up or down to AI-Generated answer). Alternatively for more detailed feedback please use https://huggingface.co/spaces/GIZ/
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- Technical development is carried out by the GIZ Data Service Center.
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- No automated bias detection – but low risk due to content restrictions.
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## 8. Contact
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For any questions, please contact via https://huggingface.co/spaces/GIZ/
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Gina Bot is an AI-powered conversational assistant designed to help you understand compliance with and analyze the Circular Economy topic. This tool leverages advanced language models to help you get clear and structured answers about Circular Economy requirements, compliance procedures, and regulatory guidance.
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It combines a generative language assistant with a knowledge base implemented via Retrieval-Augmented Generation (RAG). The scope and functionality of the tool is focused on specific document database collected by team on topic of 'Circular Economy' in context of Colombia.
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## 2. Models Used
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- The user interface clearly indicates the use of a generative AI model.
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- An explanation of the RAG method is included.
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- We collect usage statistics as detailed in Disclaimer tab of the app along with the explicit display in the user interface of the tool.
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- Feedback mechanism available (via https://huggingface.co/spaces/GIZ/gina/discussions/new).
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## 7. Monitoring, Feedback, and Incident Reporting
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- User can provide feedback via UI by giving (Thumbs-up or down to AI-Generated answer). Alternatively for more detailed feedback please use https://huggingface.co/spaces/GIZ/gina/discussions/new to report any issue.
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- Technical development is carried out by the GIZ Data Service Center.
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- No automated bias detection – but low risk due to content restrictions.
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## 8. Contact
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For any questions, please contact via https://huggingface.co/spaces/GIZ/gina/discussions/new or send us email to dataservicecenter@giz.de
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app.py
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@@ -103,6 +103,7 @@ def make_html_source(source,i):
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return card
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async def chat_response(query, history, category, request=None):
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"""Generate chat response based on method and inputs"""
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# Generate response
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response = await generate(query=query, context=context_retrieved_lst)
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# Log the interaction
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try:
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chat_logger.log(
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except Exception as e:
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print(f"Logging error: {str(e)}")
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# Stream response character by character
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displayed_response = ""
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for i, char in enumerate(
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displayed_response += char
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history[-1] = (query, displayed_response)
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yield history, docs_html
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- Always consult qualified professionals for official compliance decisions
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⚠️ **Data & Privacy:**
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- We collect usage statistics to improve the tool
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⚠️ **AI Limitations:**
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- Responses are AI-generated and may contain inaccuracies
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**Data Collection:** We collect questions, answers, feedback, and anonymized usage statistics
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to improve tool performance based on legitimate interest in service enhancement.
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By using this
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""")
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# Event Handlers
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return card
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BEGINNING_TEXT = "**Respuesta generada mediante inteligencia artificíal:** \n\n"
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async def chat_response(query, history, category, request=None):
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"""Generate chat response based on method and inputs"""
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# Generate response
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response = await generate(query=query, context=context_retrieved_lst)
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# Add disclaimer to the response
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response_with_disclaimer = BEGINNING_TEXT + response
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# Log the interaction
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try:
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chat_logger.log(
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except Exception as e:
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print(f"Logging error: {str(e)}")
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# Stream response character by character
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displayed_response = ""
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for i, char in enumerate(response_with_disclaimer):
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displayed_response += char
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history[-1] = (query, displayed_response)
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yield history, docs_html
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- Always consult qualified professionals for official compliance decisions
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⚠️ **Data & Privacy:**
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- We collect usage statistics to improve the tool.
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- No personal data is collected when using this tool.
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⚠️ **AI Limitations:**
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- Responses are AI-generated and may contain inaccuracies
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**Data Collection:** We collect questions, answers, feedback, and anonymized usage statistics
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to improve tool performance based on legitimate interest in service enhancement.
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By using this chatbot, you agree to these terms and acknowledge that you are solely responsible for any reliance on or actions taken based on its responses.
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**Technical Information**: User can read more about the technical information about the tool in [Readme](https://huggingface.co/spaces/GIZ/gina/blob/main/README.md) of this tool.
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This is just a prototype and being tested and worked upon, so its not perfect and may sometimes give irrelevant answers. If you are not satisfied with the answer, please ask a more specific question or report your feedback to help us improve the system.
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""")
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# Event Handlers
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utils/__pycache__/generator.cpython-310.pyc
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utils/__pycache__/logger.cpython-310.pyc
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utils/__pycache__/retriever.cpython-310.pyc
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