Upload setup_claude.sh with huggingface_hub

setup_claude.sh

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+#!/bin/bash
+# Setup script for Claude in VS Code on Hugging Face Space
+
+echo "Setting up Python environment for working with Claude..."
+
+# Create a virtual environment
+python -m venv ~/claude-env
+
+# Activate the virtual environment
+source ~/claude-env/bin/activate
+
+# Install required packages
+pip install -U huggingface_hub gradio transformers datasets sentence-transformers faiss-cpu torch langchain
+
+# Create initial files
+mkdir -p ~/hf_implementation
+cd ~/hf_implementation
+
+# Create a simple Gradio app
+cat > app.py << 'EOL'
+import gradio as gr
+import os
+
+def process_file(file):
+    """Process an uploaded file."""
+    filename = os.path.basename(file.name)
+    return f"File {filename} would be processed using HF models."
+
+def query_index(query):
+    """Query the RAG index."""
+    return f"Query: {query}\nResponse: This is a placeholder. The real implementation will use sentence-transformers and FAISS."
+
+# Create the Gradio interface
+with gr.Blocks(title="RAG Document Processor") as demo:
+    gr.Markdown("# RAG Document Processing System")
+    
+    with gr.Tab("Upload & Process"):
+        file_input = gr.File(label="Upload Document")
+        process_button = gr.Button("Process Document")
+        output = gr.Textbox(label="Processing Result")
+        process_button.click(process_file, inputs=file_input, outputs=output)
+    
+    with gr.Tab("Query Documents"):
+        query_input = gr.Textbox(label="Enter your query")
+        query_button = gr.Button("Search")
+        response = gr.Textbox(label="Response")
+        query_button.click(query_index, inputs=query_input, outputs=response)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+EOL
+
+# Create a sample implementation file
+cat > hf_embeddings.py << 'EOL'
+"""
+Embeddings module using sentence-transformers.
+"""
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+class HFEmbeddings:
+    def __init__(self, model_name="sentence-transformers/all-MiniLM-L6-v2"):
+        """Initialize the embedding model.
+        
+        Args:
+            model_name: Name of the sentence-transformers model to use
+        """
+        self.model = SentenceTransformer(model_name)
+        
+    def embed_texts(self, texts):
+        """Generate embeddings for a list of texts.
+        
+        Args:
+            texts: List of strings to embed
+            
+        Returns:
+            List of embedding vectors
+        """
+        return self.model.encode(texts)
+    
+    def embed_query(self, query):
+        """Generate embedding for a query string.
+        
+        Args:
+            query: Query string
+            
+        Returns:
+            Embedding vector
+        """
+        return self.model.encode(query)
+EOL
+
+# Create a README for the implementation
+cat > README.md << 'EOL'
+# Hugging Face RAG Implementation
+
+This directory contains the Hugging Face native implementation of the RAG system.
+
+## Files
+- `app.py` - Gradio interface for the RAG system
+- `hf_embeddings.py` - Embedding generation with sentence-transformers
+
+## Running the Application
+```bash
+python app.py
+```
+
+## Implementation Plan
+See `CLAUDE_HF.md` in the main directory for the complete implementation plan.
+EOL
+
+echo "Setup complete!"
+echo "To use the environment:"
+echo "1. Run 'source ~/claude-env/bin/activate'"
+echo "2. Navigate to '~/hf_implementation'"
+echo "3. Run 'python app.py' to start the Gradio interface"