added bot prediction for assessments

src/services/chatbot.py

@@ -7,6 +7,7 @@ from src.prompts.sops import *
 from src.prompts.chatbot import *
 from src.models.sop_response_schemas import *
 from src.models.bot_response_schema import *
+from scripts.assessment_data import generate_summary_stats_v2
 from dotenv import load_dotenv
 load_dotenv()
 
@@ -52,7 +53,7 @@ class Chatbot:
                     }
                 ],
                 response_format=ValidateWorker,
-                max_tokens=4096,
+                max_tokens=1024,
                 temperature=0.1
             )
 
@@ -64,3 +65,121 @@ class Chatbot:
         except Exception as e:
             print(f"An error occurred: {e}")
             return None
+        
+    
+
+
+    def predict_based_on_past_assessment(self, query, company_info, companyid) -> Result:
+        """
+        This method generates predictions based on past assessment data of a company. It queries the backend for the 
+        company's assessment data, generates a prompt, and then uses the GPT-4 model to return predictions based on the query.
+
+        :param query: The question or query asked by the user.
+        :param company_info: General information about the company (name, size, departments, etc.).
+        :param companyid: Unique identifier of the company to fetch its specific data.
+        :return: Result containing the prediction result or None if an error occurs.
+        """
+        try:
+            # Define the path to the company's assessment data (stored as a CSV)
+            data_path = os.path.join('data', 'raw', 'erp_company_assessment', f'{companyid}_raw_data.csv')
+            
+            # Generate summary statistics from the company's assessment data
+            summary_stats = generate_summary_stats_v2(file_path=data_path)
+           
+
+            # Generate the prompt using the company info and the summary statistics
+            prompt = predict_based_past_assessment_prompt(
+                query=query,
+                company_info=company_info,
+                summary_stats=summary_stats
+            )
+   
+            # Interact with GPT-4 model to get a response
+            response = self.client.beta.chat.completions.parse(
+                model=self.model,
+                messages=[
+                    {
+                        "role": "system",
+                        "content": f"{prompt}"
+                    },
+                    {
+                        "role": "user",
+                        "content": f"{query}",
+                    }
+                ],
+                response_format=Result,
+                max_tokens=1024,
+                temperature=0.1
+            )
+
+            # Extract and return the response from the GPT-4 model
+            extracted_text = json.loads(response.choices[0].message.content)
+
+            return extracted_text
+
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return None
+
+    
+    def predict_next_n_assessment(self, company_info, companyid, N) -> AssessmentPredictionsResponse:
+        """
+        This method generates predictions based on past assessment data of a company. It queries the backend for the 
+        company's assessment data, generates a prompt, and then uses the GPT-4 model to return predictions based on the query.
+
+        :param query: The question or query asked by the user.
+        :param company_info: General information about the company (name, size, departments, etc.).
+        :param companyid: Unique identifier of the company to fetch its specific data.
+        :param N: Number of assessments to predict.
+        :return: Result containing the prediction result or None if an error occurs.
+        """
+        try:
+            # Define the path to the company's assessment data (stored as a CSV)
+            data_path = os.path.join('data', 'raw', 'erp_company_assessment', f'{companyid}_raw_data.csv')
+            
+            # Generate summary statistics from the company's assessment data
+            summary_stats = generate_summary_stats_v2(file_path=data_path)
+
+            # Generate the prompt using the company info and the summary statistics
+            prompt = predict_next_n_assessments_prompt()
+          
+            
+            # Interact with GPT-4 model to get a response
+            response = self.client.beta.chat.completions.parse(
+                model=self.model,
+                messages=[
+                    {
+                        "role": "system",
+                        "content": f"{prompt}"
+                    },
+                    {
+                        "role": "user",
+                        "content": f"company info: {company_info}--> N-value is {N} ",
+                    },
+                    {
+                        "role": "user",
+                        "content": f"Summary stats: {summary_stats}",
+                    }
+                ],
+                response_format=AssessmentPredictionsResponse,
+                max_tokens=1024,
+                temperature=0.1
+            )
+
+            # Extract the response from the GPT-4 model
+            extracted_text = json.loads(response.choices[0].message.content)
+
+            # Initialize dictionary to store assessments with dynamic names
+            predictions = {}
+
+            # Loop through the predicted assessments and rename them dynamically
+            for i in range(N):
+                assessment_key = f"assessment_{i + 1}"
+                predictions[assessment_key] = extracted_text["predictions"][i]['AssessmentN']
+
+            # Return the dynamically named assessments
+            return predictions
+
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return None