Local

.github/workflows/deploy-to-hf.yml

@@ -55,4 +55,4 @@ jobs:
           cd hf-space
           git add .
           git commit -m "${{ github.event.head_commit.message || 'Update from GitHub Actions' }}"
-          git push -f origin main 
+          git push -f origin main 

auto-analyst-backend/Dockerfile

@@ -10,4 +10,23 @@ COPY --chown=user ./requirements.txt requirements.txt
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 COPY --chown=user . /app
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+# Verify agents_config.json was copied (it should be in the backend directory)
+RUN if [ -f "/app/agents_config.json" ]; then \
+        echo "✅ agents_config.json found in container"; \
+        ls -la /app/agents_config.json; \
+    else \
+        echo "⚠️ agents_config.json not found in container - will use fallback templates"; \
+        ls -la /app/ | grep -E "agents|config" || echo "No config files found"; \
+    fi
+
+# Make entrypoint script executable
+USER root
+RUN chmod +x /app/entrypoint_local.sh
+# Make populate script executable
+RUN chmod +x /app/scripts/populate_agent_templates.py
+
+USER user
+
+# Use the entrypoint script instead of directly running uvicorn
+CMD ["/app/entrypoint_local.sh"]

auto-analyst-backend/requirements.txt

@@ -1,16 +1,16 @@
 aiofiles==24.1.0
 beautifulsoup4==4.13.4
-dspy==2.6.14
+dspy==2.6.27
+litellm==1.75.2
 email_validator==2.2.0
-fastapi==0.111.1
+fastapi==0.115.5
 fastapi-cli==0.0.7
 FastAPI-SQLAlchemy==0.2.1
-fastapi-sso==0.10.0
+fastapi-sso==0.16.0
 groq==0.18.0
-gunicorn==22.0.0
+gunicorn==23.0.0
 huggingface-hub==0.30.2
 joblib==1.4.2
-litellm==1.63.7
 llama-cloud==0.1.19
 llama-cloud-services==0.6.21
 llama-index==0.12.14
@@ -29,15 +29,14 @@ matplotlib-inline==0.1.7
 numpy==2.2.2
 openpyxl==3.1.2
 xlrd==2.0.1
-openai==1.61.0
+openai==1.97.0
 pandas==2.2.3
-polars==1.30.0
+polars==1.31.0
 pillow==11.1.0
 plotly==5.24.1
 psycopg2==2.9.10
 python-dateutil==2.9.0.post0
 python-dotenv==1.0.1
-python-multipart==0.0.9
 requests==2.32.3
 scikit-learn==1.6.1
 scipy==1.15.1
@@ -51,13 +50,14 @@ tiktoken==0.8.0
 tokenizers==0.21.0
 tqdm==4.67.1
 urllib3==2.4.0
-uvicorn==0.22.0
-websockets==14.2
+uvicorn==0.29.0
+websockets>=13.1.0
 wheel==0.45.1
 xgboost-cpu==3.0.2
 bokeh==3.7.3
 pymc==5.23.0
 lightgbm==4.6.0
 arviz==0.21.0
 optuna==4.3.0
-shap==0.45.1
+shap==0.45.1
+litellm[proxy]

auto-analyst-backend/scripts/format_response.py

@@ -856,7 +856,7 @@ def format_plan_instructions(plan_instructions):
         else:
             raise TypeError(f"Unsupported plan instructions type: {type(plan_instructions)}")
     except Exception as e:
-        raise ValueError(f"Error processing plan instructions: {str(e)}")
+        raise ValueError(f"Error processing plan instructions: {str(e)} + {dspy.settings.lm} ")
     # logger.log_message(f"Plan instructions: {instructions}", level=logging.INFO)
 
 
@@ -1059,7 +1059,7 @@ def format_response_to_markdown(api_response, agent_name = None, dataframe=None)
 
     except Exception as e:
         logger.log_message(f"Error in format_response_to_markdown: {str(e)}", level=logging.ERROR)
-        return f"{str(e)}"
+        return f"error formating markdown {str(e)}"
 
     # logger.log_message(f"Generated markdown content for agent '{agent_name}' at {time.strftime('%Y-%m-%d %H:%M:%S')}: {markdown}, length: {len(markdown)}", level=logging.INFO)
 
@@ -1070,7 +1070,7 @@ def format_response_to_markdown(api_response, agent_name = None, dataframe=None)
             f"API Response: {api_response}",
             level=logging.ERROR
         )
-        return " "
+        return ""
 
     return '\n'.join(markdown)
 
@@ -1100,4 +1100,4 @@ def format_response_to_markdown(api_response, agent_name = None, dataframe=None)
         }
     }
 
-    formatted_md = format_response_to_markdown(sample_response)
+    formatted_md = format_response_to_markdown(sample_response)