added goal achievment preditions

src/services/chatbot.py

@@ -183,3 +183,94 @@ class Chatbot:
         except Exception as e:
             print(f"An error occurred: {e}")
             return None
+
+
+    def recommend_assessment_frequencies(self,sops) -> AssessmentSuggestion:
+        """
+    
+        Process the SOPs data and return assessment frequencies.
+        
+
+        """
+        try:
+
+            chunk_size = 1000  # Define your chunk size
+        # Convert the 'sops' dictionary values into a single text string for chunking
+            sops_text = '\n'.join([str(item) for sublist in sops.values() for item in sublist])
+            
+            # Break the text into chunks of the defined size
+            docs_text = [sops_text[i:i + chunk_size] for i in range(0, len(sops_text), chunk_size)]
+
+            # Create a list of documents
+            docs = [{"type": "text", "text": text} for text in docs_text]
+            # Generate the prompt using the company info and the summary statistics
+            
+            prompt = recommend_assessment_frequency_prompt()  # Update your prompt to handle managers and workers
+                    
+            response = self.client.beta.chat.completions.parse(
+                model=self.model,
+                messages=[
+                    {"role": "system", "content": prompt},
+                    {"role": "user", "content": docs}
+                ],
+                response_format=AssessmentSuggestion,  # Use the updated response schema
+                max_tokens=4096,
+                temperature=0.1
+            )
+            
+            return json.loads(response.choices[0].message.content)
+
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return None
+
+
+    def predict_goal_achievement_probability(self, company_info, companyid) -> AchievementPrediction:
+        """
+        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 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_goal_achievement_probability_prompt()
+          
+            # Interact with GPT-4 model to get a response
+            response = self.client.beta.chat.completions.parse(
+                model=self.model,
+                messages=[
+                    {
+                        "role": "system",
+                        "content": prompt
+                    },
+                    {
+                        "role": "user",
+                        "content": f"company info: {company_info}",
+                    },
+                    {
+                        "role": "user",
+                        "content": f"Summary stats: {summary_stats}",
+                    }
+                ],
+                response_format=AchievementPrediction,
+                max_tokens=1024,
+                temperature=0.1
+            )
+
+            # Extract the response from the GPT-4 model
+            predictions = json.loads(response.choices[0].message.content)
+            # Return the dynamically named assessments
+            return predictions
+
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return None