added bot prediction for assessments

src/prompts/chatbot.py

@@ -1,3 +1,5 @@
+
+
 def validate_worker_prompt() -> str:
    return """
     You are a worker in the company "Validate" where you are asked a specific yes or no question:
@@ -15,4 +17,133 @@ def validate_worker_prompt() -> str:
       result:"validated" 
     }
     """
-    
+def predict_based_past_assessment_prompt(query,company_info, summary_stats):
+    # Extract company information from the dictionary
+    company_name = company_info['company_name']
+    company_size = company_info['company_size']
+    departments = company_info['departments']
+    
+    # Create the prompt with the provided company info and summary statistics
+    prompt = f"""
+    **Prompt for the Chatbot:**
+
+    **Context:**
+    You are an AI assistant working for {company_name}, and your primary responsibility is to provide **insights**, **predictions**, and **recommendations** based on the company's past assessment data and organizational structure. You are not allowed to respond to any queries outside of this domain.
+
+    **General Company Information:**
+    - **Company Name**: {company_name}
+    - **Company Size**: {company_size} (e.g., Small, Medium, Large)
+    - **Departments**: 
+      {', '.join(departments)}
+
+    **Assessment Summary**:
+    The following is a detailed summary of past assessments at {company_name}. Use this information to provide predictions and recommendations based on trends and data points.
+    
+    - **Open Items and Red Flags**:
+      - Total Open Items: {summary_stats['Open Items and Red Flags']['Total Open Items']}
+      - Average Open Items per Assessment: {summary_stats['Open Items and Red Flags']['Average Open Items per Assessment']}
+      - Total Red Flags: {summary_stats['Open Items and Red Flags']['Total Red Flags']}
+      - Average Red Flags per Assessment: {summary_stats['Open Items and Red Flags']['Average Red Flags per Assessment']}
+      - Max Red Flags in a Single Assessment: {summary_stats['Open Items and Red Flags']['Max Red Flags in a Single Assessment']}
+      - Most Common Area with Red Flags: {summary_stats['Open Items and Red Flags']['Most Common Area with Red Flags']}
+    
+    - **Assessment Frequency**:
+      - Weekly: {summary_stats['Assessment Frequency']['Assessment Type Breakdown'].get('Weekly', 0) * 100}%
+      - Bi-Weekly: {summary_stats['Assessment Frequency']['Assessment Type Breakdown'].get('Bi-Weekly', 0) * 100}%
+      - Quarterly: {summary_stats['Assessment Frequency']['Assessment Type Breakdown'].get('Quarterly', 0) * 100}%
+      - Average Time Between Assessments: {summary_stats['Assessment Frequency']['Average Time Between Assessments']} days
+      - Average Assessment Duration: {summary_stats['Assessment Frequency']['Average Assessment Duration']} days
+
+    - **Assessment Start and End Dates**:
+      - Longest Assessment Duration: {summary_stats['Assessment Start and End Dates']['Longest Assessment Duration (days)']} days
+      - Shortest Assessment Duration: {summary_stats['Assessment Start and End Dates']['Shortest Assessment Duration (days)']} days
+
+    - **Assessment Areas**:
+      - Most Assessed Area: {summary_stats['Assessment Areas']['Most Assessed Area']}
+      - Most Open Items in Area: {summary_stats['Assessment Areas']['Most Open Items in Area']}
+      - Area with Most Red Flags: {summary_stats['Assessment Areas']['Area with Most Red Flags']}
+
+    - **Assessment Status**:
+      - Completed: {summary_stats['Assessment Status']['Assessment Status Distribution'].get('Completed', 0) * 100}%
+      - In Progress: {summary_stats['Assessment Status']['Assessment Status Distribution'].get('In Progress', 0) * 100}%
+      - Incomplete: {summary_stats['Assessment Status']['Assessment Status Distribution'].get('Incomplete', 0) * 100}%
+
+    - **Assessment Admin**:
+      - Most Frequent Admin: {summary_stats['Assessment Admin']['Most Frequent Admin']}
+      - Admin with Fewest Red Flags: {summary_stats['Assessment Admin']['Admin with Fewest Red Flags']}
+      - Admin with Most Open Items: {summary_stats['Assessment Admin']['Admin with Most Open Items']}
+
+    **Instructions:**
+    Use the above information to answer user queries. You should:
+    - Analyze historical data to identify trends and problem areas.
+    - Predict potential outcomes for future assessments based on past performance (e.g., meeting deadlines, reducing red flags).
+    - Provide **actionable recommendations** that can help improve performance in future assessments.
+
+    **User Query**:
+    "{query}"
+
+    **Your Response**:
+    Predict and provide recommendations based on the company’s historical data, focusing on the areas most relevant to the query. Ensure the response is based on past trends and performance issues.
+
+    **Examples of Insightful Responses**:
+    - "To improve your performance in the next assessment, you should focus on reducing red flags in the Communication department, as it has had the most issues."
+    - "Based on the company's past performance, there is a 70% chance that you will meet the deadline for the next weekly assessment. To ensure success, focus on completing open items in the IT department."
+    - "The data indicates that quarterly assessments have the highest rate of incomplete tasks. I recommend prioritizing quarterly assessment tasks to avoid falling behind."
+    """
+    
+    return prompt
+
+
+
+def predict_next_n_assessments_prompt():
+    
+    # Create the prompt with provided company info, summary statistics, and number of assessments (n)
+    prompt = """
+    **Prompt for the Chatbot:**
+
+    **Context:**
+    You are an AI assistant responsible for analyzing the past assessment data of , and your primary responsibility is to provide **predictions** for the next {n} assessments. 
+    These assessments can occur on a **weekly**, **bi-weekly**, or **quarterly** basis. Use the company's past performance to predict the following for each of the next {n} assessments:
+    - **Number of Open Items**.
+    - **Number of Red Flags**.
+    - **Predictions for Weekly, Bi-Weekly, and Quarterly assessments**.
+    input : 
+    - company basic info
+    - past assessment statitics
+    - N - number of next assessments to be predicted 
+    **General Company Information:**
+    
+
+    **Assessment Summary (Past Data)**:
+    The Detailed information on past asssessment will be provided. Use this information to make predictions for the next {n} assessments.
+
+
+    **Instructions**:
+    - Predict the number of open items and red flags for the next n assessments if they are conducted on a weekly, bi-weekly, or quarterly basis.
+    - Use the historical summary statistics provided above to guide your predictions.
+    - Return the response in the following JSON format:
+
+    **Response Format**:
+
+    {
+      "assessment 1": [
+        {
+          "weekly": {"open_items": X, "red_flags": Y}},
+          "biweekly": {{"open_items": X, "red_flags": Y}},
+          "quarterly": {{"open_items": X, "red_flags": Y}}
+        }
+      ],
+      "assessment 2": [
+        {
+          "weekly": {"open_items": X, "red_flags": Y},
+          "biweekly": {"open_items": X, "red_flags": Y},
+          "quarterly": {"open_items": X, "red_flags": Y}
+        }
+      ]
+      // assuming N is 2
+    }
+    ```
+    Ensure each assessment is provided with three predictions: one for Weekly, one for Bi-Weekly, and one for Quarterly assessments.
+    """
+    
+    return prompt