Update app.py

app.py

@@ -1,5 +1,5 @@
 # ------------------------------
-# UniversalResearch AI System with Refinement Counter and Increased Recursion Limit
+# UniversalResearch AI System with Refinement Counter and DEEPSEEK_API_KEY
 # ------------------------------
 import logging
 import os
@@ -53,15 +53,14 @@ class AgentState(TypedDict):
 class ResearchConfig:
     """
     Generic configuration for the UniversalResearch AI System.
-    This configuration is designed to be applicable to any research domain.
+    Make sure to set DEEPSEEK_API_KEY in your environment or HF Space secrets.
     """
-    DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY")
+    DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY")  # Updated to reference DEEPSEEK_API_KEY
     CHROMA_PATH = "chroma_db"
     CHUNK_SIZE = 512
     CHUNK_OVERLAP = 64
     MAX_CONCURRENT_REQUESTS = 5
     EMBEDDING_DIMENSIONS = 1536
-    # An optional map can be used to list pre-loaded or featured research topics.
     DOCUMENT_MAP = {
         "Sample Research Document 1": "Topic A Overview",
         "Sample Research Document 2": "Topic B Analysis",
@@ -78,11 +77,12 @@ class ResearchConfig:
         "Format your response in Markdown with LaTeX mathematical notation where applicable."
     )
 
+# Early check for missing API key
 if not ResearchConfig.DEEPSEEK_API_KEY:
     st.error(
         """**Research Portal Configuration Required**  
 1. Obtain your DeepSeek API key from [platform.deepseek.com](https://platform.deepseek.com/)  
-2. Set the secret: `DEEPSEEK_API_KEY` in your deployment settings  
+2. Set the secret: `DEEPSEEK_API_KEY` in your Space settings  
 3. Rebuild your deployment."""
     )
     st.stop()
@@ -92,8 +92,8 @@ if not ResearchConfig.DEEPSEEK_API_KEY:
 # ------------------------------
 class UniversalDocumentManager:
     """
-    Manages the creation of document collections for any research domain.
-    Documents are split into manageable chunks and embedded using OpenAI embeddings.
+    Manages creation of document collections for any research domain.
+    Documents are split into chunks and embedded using OpenAI embeddings.
     """
     def __init__(self) -> None:
         try:
@@ -140,7 +140,6 @@ class UniversalDocumentManager:
 
 # Initialize document collections for multiple research domains
 udm = UniversalDocumentManager()
-# Example collections – these can be updated with any research domain documents.
 research_docs = udm.create_collection([
     "Research Report: Novel AI Techniques in Renewable Energy",
     "Academic Paper: Advances in Quantum Computing for Data Analysis",
@@ -326,7 +325,11 @@ class ResearchWorkflow:
             logger.info(f"Retrieved {len(docs)} documents for query.")
             return {
                 "messages": [AIMessage(content=f"Retrieved {len(docs)} documents")],
-                "context": {"documents": docs, "retrieval_time": time.time(), "refine_count": state["context"].get("refine_count", 0)}
+                "context": {
+                    "documents": docs,
+                    "retrieval_time": time.time(),
+                    "refine_count": state["context"].get("refine_count", 0)
+                }
             }
         except Exception as e:
             return self._error_state(f"Retrieval Error: {str(e)}")
@@ -344,8 +347,15 @@ class ResearchWorkflow:
                 return self._error_state(response["error"])
             logger.info("Content analysis completed.")
             return {
-                "messages": [AIMessage(content=response.get('choices', [{}])[0].get('message', {}).get('content', ''))],
-                "context": {"analysis": response, "refine_count": state["context"].get("refine_count", 0)}
+                "messages": [
+                    AIMessage(
+                        content=response.get('choices', [{}])[0].get('message', {}).get('content', '')
+                    )
+                ],
+                "context": {
+                    "analysis": response,
+                    "refine_count": state["context"].get("refine_count", 0)
+                }
             }
         except Exception as e:
             return self._error_state(f"Analysis Error: {str(e)}")
@@ -363,7 +373,11 @@ class ResearchWorkflow:
         response = self.processor.process_query(validation_prompt)
         logger.info("Output validation completed.")
         return {
-            "messages": [AIMessage(content=analysis + f"\n\nValidation: {response.get('choices', [{}])[0].get('message', {}).get('content', '')}")]
+            "messages": [
+                AIMessage(
+                    content=analysis + f"\n\nValidation: {response.get('choices', [{}])[0].get('message', {}).get('content', '')}"
+                )
+            ]
         }
 
     def refine_results(self, state: AgentState) -> Dict:
@@ -381,7 +395,11 @@ class ResearchWorkflow:
         response = self.processor.process_query(refinement_prompt)
         logger.info("Refinement completed.")
         return {
-            "messages": [AIMessage(content=response.get('choices', [{}])[0].get('message', {}).get('content', ''))],
+            "messages": [
+                AIMessage(
+                    content=response.get('choices', [{}])[0].get('message', {}).get('content', '')
+                )
+            ],
             "context": state["context"]
         }
 
@@ -480,7 +498,6 @@ class ResearchInterface:
         with st.sidebar:
             st.title("🔍 Research Database")
             st.subheader("Featured Research Topics")
-            # Display featured research topics from the DOCUMENT_MAP.
             for title, short in ResearchConfig.DOCUMENT_MAP.items():
                 with st.expander(short):
                     st.markdown(f"```\n{title}\n```")