added goal achievment preditions

src/services/questions_generator.py

@@ -57,7 +57,7 @@ class QuestionsGenerator:
                 )
                 
                 questions = json.loads(response.choices[0].message.content)
-                print(f"Generated questions: {questions}")
+                
                 all_questions.append({
                     "frequency_number": frequency_label,
                     "questions": questions
@@ -67,3 +67,53 @@ class QuestionsGenerator:
         
         except json.JSONDecodeError:
             return False
+
+
+    def generate_questions_v2(self, input_data):
+        try:
+            sops = input_data['sops']
+            assessment_type = input_data['assessment_type']
+            frequency_type = input_data['frequency_type']
+            frequency_number = input_data['frequency_number']  # Specific week for which to generate questions
+            total_duration = input_data['duration']
+            roles_data = input_data["roles_data"]
+
+            # Chunk the SOPs into smaller pieces
+            chunk_size = 1000  # Define your chunk size
+            docs_text = [sops[i:i + chunk_size] for i in range(0, len(sops), chunk_size)]
+            # Create a list of documents
+            docs = [{"type": "text", "text": text} for text in docs_text]
+
+            prompt = get_questions_prompt()  # Get the questions prompt for the SOP
+
+            # Prepare the frequency label
+            frequency_label = f"{frequency_type} {frequency_number}"  # e.g., week 2
+
+            # Generate questions for the current frequency number only
+            response = self.client.beta.chat.completions.parse(
+                model=self.model,
+                messages=[
+                    {"role": "system", "content": prompt},
+                    {"role": "user", "content": f"The SOPs are provided below."},
+                    {"role": "user", "content": json.dumps(docs)},  # Use the chunked documents
+                    {"role": "user", "content": f"Assessment Type: {assessment_type}"},
+                    {"role": "user", "content": f"Frequency Type: {frequency_type}"},
+                    {"role": "user", "content": f"Current Frequency Number to generate: {frequency_label}"},
+                    {"role": "user", "content": f"Duration: {total_duration}"},
+                    {"role": "user", "content": f"Roles Data: {roles_data}"}
+                ],
+                response_format=AssementQuestion,  # Ensure you specify the correct format
+                max_tokens=4096,
+                temperature=0.1
+            )
+            
+            # Parse and format the response
+            questions = json.loads(response.choices[0].message.content)
+            
+            return {
+                "frequency_number": frequency_label,
+                "questions": questions
+            }
+
+        except json.JSONDecodeError:
+            return False