changed according to spaces

README.md

@@ -14,7 +14,7 @@ pinned: false
 
 A Python demo chatbot that:
 
-- loads `config.yaml` with `sambanova_api_key` and `website`
+- loads configuration from environment variables or `config.yaml`
 - scrapes the configured website
 - builds embeddings using HuggingFace models
 - retrieves relevant chunks (RAG)
@@ -28,11 +28,13 @@ A Python demo chatbot that:
    pip install -r requirements.txt
    ```
 
-2. Configure `config.yaml`:
-   - `sambanova_api_key`: your SambaNova API key
-   - `website`: the URL to scrape
-   - `embedding_model`: HuggingFace model (default: `sentence-transformers/all-MiniLM-L6-v2`)
-   - `system_prompt`: optional behavior prompt
+2. Configure environment variables:
+   - `SAMBANOVA_API_KEY`: your SambaNova API key
+   - `WEBSITE`: the URL to scrape
+   - `EMBEDDING_MODEL`: HuggingFace model (default: `sentence-transformers/all-MiniLM-L6-v2`)
+   - `SYSTEM_PROMPT`: optional behavior prompt
+
+   Or create `config.yaml` with these keys.
 
 ## Run CLI Mode
 
@@ -49,3 +51,15 @@ python app.py
 ```
 
 Interactive web interface with real-time answers and citations display.
+
+## Hugging Face Spaces
+
+For deployment on Hugging Face Spaces:
+
+1. Set the following secrets in your Space settings:
+   - `SAMBANOVA_API_KEY`
+   - `WEBSITE`
+   - `EMBEDDING_MODEL` (optional)
+   - `SYSTEM_PROMPT` (optional)
+
+2. The app will automatically use these environment variables.

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import os
 from pathlib import Path
 from sambanova import SambaNova
 from langchain_huggingface import HuggingFaceEmbeddings
@@ -20,19 +21,28 @@ def init_resources():
     if RESOURCE_STATE:
         return RESOURCE_STATE
 
-    if not CONFIG_PATH.exists():
-        raise FileNotFoundError(f"Missing config file: {CONFIG_PATH}")
+    # Try to load from environment variables first (for Spaces)
+    llm_api_key = os.environ.get("SAMBANOVA_API_KEY")
+    website = os.environ.get("WEBSITE")
+    embedding_model_name = os.environ.get("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+    system_prompt = os.environ.get("SYSTEM_PROMPT", "You are a helpful assistant.")
 
-    config = load_config(CONFIG_PATH)
-    llm_api_key = config.get("sambanova_api_key")
-    website = config.get("website")
-    system_prompt = config.get("system_prompt", "You are a helpful assistant.")
+    # Fallback to config.yaml if env vars not set
+    if not llm_api_key or not website:
+        if CONFIG_PATH.exists():
+            config = load_config(CONFIG_PATH)
+            llm_api_key = llm_api_key or config.get("sambanova_api_key")
+            website = website or config.get("website")
+            embedding_model_name = embedding_model_name or config.get("embedding_model", "sentence-transformers/all-MiniLM-L6-v2")
+            system_prompt = system_prompt or config.get("system_prompt", "You are a helpful assistant.")
+        else:
+            raise ValueError("Please set SAMBANOVA_API_KEY and WEBSITE environment variables or provide config.yaml")
 
     if not llm_api_key or not website:
-        raise ValueError("Please set sambanova_api_key and website in config.yaml")
+        raise ValueError("SAMBANOVA_API_KEY and WEBSITE are required")
 
-    embed_model = HuggingFaceEmbeddings(model_name=config.get("embedding_model"))
-    corpus = build_rag_corpus(config, embed_model, website)
+    embed_model = HuggingFaceEmbeddings(model_name=embedding_model_name)
+    corpus = build_rag_corpus({"embedding_model": embedding_model_name}, embed_model, website)
     client = SambaNova(
         api_key=llm_api_key,
         base_url="https://api.sambanova.ai/v1",
@@ -40,7 +50,7 @@ def init_resources():
     )
 
     RESOURCE_STATE.update(
-        config=config,
+        config={"embedding_model": embedding_model_name},
         website=website,
         system_prompt=system_prompt,
         embed_model=embed_model,