Delete prompts.py

prompts.py

@@ -1,126 +0,0 @@
-ENHANCED_SYSTEM_PROMPT = """You are an advanced data analysis assistant. Respond ONLY in valid JSON format.
-
-CHART CREATION RULES:
-- For visualization requests (chart, graph, plot, visualize): Always include "plot" object
-- For informational queries (explain, describe, what is, count): Set "plot": null
-- For statistical analysis without charts: Set "plot": null
-
-RESPONSE FORMATS:
-
-1. INFORMATIONAL (no visualization):
-{
-  "type": "explain",
-  "operations": [],
-  "plot": null,
-  "narrative": "detailed answer",
-  "insights_needed": false
-}
-
-2. STATISTICAL DESCRIPTION:
-{
-  "type": "describe", 
-  "operations": [{"op": "describe", "columns": ["col1", "col2"]}],
-  "plot": null,
-  "narrative": "statistical summary",
-  "insights_needed": false
-}
-
-3. VISUALIZATION REQUEST:
-{
-  "type": "analysis",
-  "operations": [
-    {"op": "groupby", "columns": ["category"], "agg_col": "value", "agg_func": "sum"}
-  ],
-  "plot": {
-    "type": "bar|line|pie|hist|scatter",
-    "x": "category", 
-    "y": "sum_value",
-    "title": "Chart Title"
-  },
-  "narrative": "brief explanation",
-  "insights_needed": true
-}
-
-4. FILTERING:
-{
-  "type": "analysis",
-  "operations": [{"op": "filter", "expr": "Age > 25"}],
-  "plot": null,
-  "narrative": "filtered data explanation",
-  "insights_needed": false
-}
-
-5. CALCULATIONS:
-{
-  "type": "analysis", 
-  "operations": [{"op": "calculate", "expr": "Col1 * Col2", "new_col": "Product"}],
-  "plot": null,
-  "narrative": "calculation explanation",
-  "insights_needed": false
-}
-
-CHART TYPES:
-- "bar": For categorical comparisons
-- "line": For trends over time/sequence
-- "pie": For proportions/percentages
-- "hist": For distributions
-- "scatter": For correlations
-
-Always ensure column names exist in the dataset before referencing them.
-"""
-
-INSIGHTS_SYSTEM_PROMPT = "You are a data insights expert."
-
-SAMPLE_QUESTIONS = [
-    "What are the key patterns in this dataset?",
-    "Explain the data structure and what insights can be derived",
-    "What are the most important findings from this data?",
-    "Compare the relationships between different columns",
-    "Which columns have the strongest influence on the data?",
-    "What trends can you identify in the dataset?",
-    "Generate insights about data quality and completeness",
-    "What recommendations would you make based on this data?",
-    "Identify any anomalies or outliers in the dataset"
-]
-
-def get_chart_prompt(question, columns, data_sample):
-    return f"""
-Question: {question}
-
-Available Columns: {', '.join(columns)}
-
-Sample Data:
-{data_sample}
-
-Create a JSON response following the format rules. If the question asks for visualization, include proper "plot" object with correct column names.
-"""
-
-def validate_plot_spec(plot_spec, available_columns):
-    if not plot_spec:
-        return plot_spec
-    
-    x_col = plot_spec.get('x')
-    y_col = plot_spec.get('y')
-    
-    if x_col and x_col not in available_columns:
-        for col in available_columns:
-            if any(keyword in col.lower() for keyword in ['name', 'category', 'type', 'group']):
-                plot_spec['x'] = col
-                break
-    
-    if y_col and y_col not in available_columns:
-        for col in available_columns:
-            if any(keyword in col.lower() for keyword in ['value', 'amount', 'count', 'price', 'sales']):
-                plot_spec['y'] = col
-                break
-    
-    return plot_spec
-
-def get_insights_prompt(context_parts, narrative):
-    insights_context = "\n".join(context_parts)
-    return f"""Based on this analysis, provide 4-6 detailed bullet points explaining key insights, patterns, and findings.
-Analysis Context:
-{insights_context}
-Original Question Context:
-{narrative}
-Provide insights as bullet points."""