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@@ -21,8 +21,12 @@ EXPOSE 7860
 
 # Set environment variables
 ENV CREWAI_TELEMETRY_ENABLED=false
+ENV CREWAI_STORAGE_DIR=/tmp/crewai
 ENV GRADIO_SERVER_NAME=0.0.0.0
 ENV GRADIO_SERVER_PORT=7860
 
+# Create necessary directories
+RUN mkdir -p /tmp/crewai
+
 # Command to run the Gradio app
 CMD ["python", "app.py"]

agents.py

@@ -1,4 +1,9 @@
 # agents.py
+import os
+
+# Set CrewAI storage directory to something writable BEFORE any imports
+os.environ["CREWAI_STORAGE_DIR"] = "/tmp/crewai"
+os.environ["CREWAI_TELEMETRY_ENABLED"] = "false"
 
 from utils import gemini_llm
 from crewai import Agent

app.py

@@ -1,14 +1,19 @@
  # interface.py
 import os
+
+# Set CrewAI storage directory to something writable BEFORE any imports
+os.environ["CREWAI_STORAGE_DIR"] = "/tmp/crewai"
+os.environ["CREWAI_TELEMETRY_ENABLED"] = "false"
+
+# Create the directory if it doesn't exist
+os.makedirs("/tmp/crewai", exist_ok=True)
+
 import gradio as gr
 from crewai import Crew, Process
 from agents import finance_knowledge_agent, market_news_agent, stock_analysis_agent, response_refiner_agent
 from tasks import get_finance_knowledge_task, get_market_news_task, get_stock_analysis_task, get_response_refiner_task
 from utils import determine_question_type, search_qdrant
 
-# Set CrewAI storage directory to something writable
-os.environ["CREWAI_STORAGE_DIR"] = "/tmp/crewai"
-
 # Initialize Crew 
 finance_crew = Crew(
     agents=[finance_knowledge_agent, market_news_agent, stock_analysis_agent, response_refiner_agent],

main.py

@@ -0,0 +1,63 @@
+# main.py
+import os
+
+# Set CrewAI storage directory to something writable BEFORE any imports
+os.environ["CREWAI_STORAGE_DIR"] = "/tmp/crewai"
+os.environ["CREWAI_TELEMETRY_ENABLED"] = "false"
+
+from crewai import Crew, Process
+from agents import finance_knowledge_agent, market_news_agent, stock_analysis_agent, response_refiner_agent
+from tasks import get_finance_knowledge_task, get_market_news_task, get_stock_analysis_task, get_response_refiner_task
+from utils import determine_question_type, search_qdrant
+
+def main():
+    """Main function to run the finance chatbot in terminal."""
+    finance_crew = Crew(
+        agents=[finance_knowledge_agent, market_news_agent, stock_analysis_agent, response_refiner_agent],
+        tasks=[],
+        process=Process.sequential,
+        verbose=True
+    )
+
+    print("📈 Welcome to the Finance Chatbot!")
+    print("Examples: 'What is investing?', 'Analyze AAPL', 'What’s the latest market news?'")
+    while True:
+        query = input("Enter your query (type 'exit' to quit): ").strip()
+        if query.lower() == "exit":
+            print("Goodbye!")
+            break
+
+        try:
+            question_type, processed_query = determine_question_type(query)
+            finance_crew.tasks = []  # Reset tasks for each query
+
+            rag_note = "RAG_SUFFICIENT"  # Default value
+            if question_type == "finance_knowledge":
+                contexts = search_qdrant(query, top_k=3)
+                context_text = "\n\n".join([f"Source: {ctx['source']}\nContent: {ctx['text']}" for ctx in contexts])
+                is_context_useful = len(context_text) > 50 and any(query.lower() in ctx["text"].lower() for ctx in contexts)
+                rag_note = "RAG_NOT_USED" if not is_context_useful else "RAG_SUFFICIENT"
+                initial_task = get_finance_knowledge_task(query)
+            elif question_type == "market_news":
+                rag_note = "NO_RAG_NEEDED"  # Market news doesn't use RAG
+                initial_task = get_market_news_task(query)
+            elif question_type == "stock_analysis":
+                rag_note = "NO_RAG_NEEDED"  # Stock analysis doesn't use RAG
+                initial_task = get_stock_analysis_task(processed_query)
+            else:
+                initial_task = get_finance_knowledge_task(query)  # Default
+
+            finance_crew.tasks.append(initial_task)
+            initial_response = finance_crew.kickoff()
+
+            refiner_task = get_response_refiner_task(query, initial_response, question_type, rag_note=rag_note)
+            finance_crew.tasks = [refiner_task]
+            final_report = finance_crew.kickoff()
+
+            print(f"\nFinal Report:\n{final_report}\n")
+        except Exception as e:
+            print(f"Error processing query: {e}")
+            print("Please try again with a different query.\n")
+
+if __name__ == "__main__":
+    main()

tasks.py

@@ -1,4 +1,9 @@
 # tasks.py
+import os
+
+# Set CrewAI storage directory to something writable BEFORE any imports
+os.environ["CREWAI_STORAGE_DIR"] = "/tmp/crewai"
+os.environ["CREWAI_TELEMETRY_ENABLED"] = "false"
 
 from utils import search_qdrant, search_news, get_stock_data
 from crewai import Task

utils.py

@@ -1,6 +1,11 @@
 # utils.py
 
 import os
+
+# Set CrewAI storage directory to something writable BEFORE any imports
+os.environ["CREWAI_STORAGE_DIR"] = "/tmp/crewai"
+os.environ["CREWAI_TELEMETRY_ENABLED"] = "false"
+
 from dotenv import load_dotenv
 from langchain_qdrant import QdrantVectorStore
 from langchain_community.embeddings import HuggingFaceEmbeddings