Update pages/4-Create your own bot (advanced).py

pages/4-Create your own bot (advanced).py

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+import streamlit as st
+
+def developer_guide():
+    st.title("For Developers: Create Your Own Bot that Knows Your CV with Hugging Face and RAG")
+    st.subheader("Build Intelligent CV-Aware Assistants with Retrieval-Augmented Generation")
+
+    st.markdown("""
+    ### Introduction
+    
+    This guide is designed for developers who want to go beyond CV enhancement and create personalized bots that can intelligently interact with and understand their CV. By combining the power of Hugging Face models with Retrieval-Augmented Generation (RAG), you can build bots capable of fetching relevant information from your CV and generating insightful, context-aware responses.
+    
+    If you're ready to dive into the technical side of AI-driven CV assistants, this step-by-step guide will help you get started.
+    """)
+
+    st.markdown("""
+    ### What is Retrieval-Augmented Generation (RAG)?
+    
+    RAG is a technique that combines traditional information retrieval with powerful generation models, such as GPTs. It allows a model to "retrieve" relevant information from a predefined dataset (like your CV) before generating responses. This ensures that the bot can reference specific details from your CV, making it highly personalized and accurate.
+    
+    With RAG, your bot won't just generate generic answers—it will craft responses based on your unique career history and experiences.
+    """)
+
+    st.markdown("""
+    ### Step-by-Step Guide to Building Your RAG-based CV Assistant
+    
+    **Step 1: Prepare Your CV Data**  
+    Convert your CV into a structured format such as JSON, CSV, or even plain text. Break it down into logical sections like work experience, education, skills, and achievements. This data will be used as the retrieval base for the RAG model.
+    
+    Example structure:
+    
+    ```json
+    {
+      "experience": [
+        {"title": "Software Engineer", "company": "TechCorp", "years": "2019-2022", "details": "Worked on AI solutions..."},
+        {"title": "Data Scientist", "company": "DataWorks", "years": "2017-2019", "details": "Led data projects..."}
+      ],
+      "education": [
+        {"degree": "MSc in Computer Science", "institution": "XYZ University", "years": "2015-2017"}
+      ],
+      "skills": ["Python", "Machine Learning", "NLP"]
+    }
+    ```
+
+    **Step 2: Use Hugging Face's Transformers for RAG**  
+    Use Hugging Face's `transformers` library to load a pre-trained RAG model. This model will handle both the retrieval of relevant sections of your CV and the generation of a coherent response based on the user’s input.
+    
+    Example setup:
+
+    ```python
+    from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
+    
+    # Load tokenizer and model
+    tokenizer = RagTokenizer.from_pretrained('facebook/rag-token-base')
+    retriever = RagRetriever.from_pretrained('facebook/rag-token-base', index_name='custom', passages=[...])
+    model = RagTokenForGeneration.from_pretrained('facebook/rag-token-base', retriever=retriever)
+
+    # Tokenize user input
+    inputs = tokenizer("Tell me about my work at TechCorp", return_tensors="pt")
+
+    # Generate a response using RAG
+    generated = model.generate(input_ids=inputs['input_ids'])
+    response = tokenizer.batch_decode(generated, skip_special_tokens=True)
+    print(response)
+    ```
+
+    **Step 3: Customize the Retrieval**  
+    You'll need to define how your bot retrieves the most relevant sections of your CV. This can involve tweaking the retriever’s configuration or pre-processing your CV data to improve retrieval accuracy.
+    
+    You can use embeddings and similarity search to match the user's queries with specific sections of the CV.
+    
+    **Step 4: Deploy Your Bot**  
+    Once your bot is working, you can deploy it using Hugging Face's Inference API or host it as a service. You can integrate it into your website or app to interact with your users.
+    
+    Consider embedding the bot in your personal website’s career page, or creating a chatbot to handle CV-related questions for job applications.
+
+    **Step 5: Improve with Feedback**  
+    Continuously improve your bot by adding more CV data, refining the retrieval process, or training a fine-tuned RAG model to better understand the language of your career domain.
+    """)
+
+    st.markdown("""
+    ### Ready to Build?
+    Follow these steps, and you'll have a personalized CV assistant bot powered by Hugging Face and RAG. Start building now, and see how AI can revolutionize the way you present your career!
+
+    For more details on RAG and Hugging Face models, check the [official Hugging Face documentation](https://huggingface.co/docs/transformers/model_doc/rag).
+    """)
+
+# Call the function to display the developer guide page
+developer_guide()