added worker validation bot

src/services/chatbot.py

@@ -0,0 +1,66 @@
+import os
+import json
+from openai import OpenAI
+from pydantic import BaseModel, Field
+from typing import List, Dict, Optional
+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 dotenv import load_dotenv
+load_dotenv()
+
+
+#SopGeneratorDocument
+class Chatbot:
+    def __init__(self):
+        self.api_key = os.getenv("OPENAI_API_KEY")
+        self.client = OpenAI(api_key=self.api_key)
+        self.model = "gpt-4o-mini"
+
+    def _extract_text_from_docs(self, docs):
+        """Extract text content from document objects."""
+        return [doc.page_content for doc in docs]
+    # Existing methods...
+
+    def validate_worker(self, question, docs) -> VisionMissionResponse:
+        """
+        This method is responsible for validating the worker's response ("yes" or "no") using the provided document(s).
+        The system generates a prompt, extracts document content, and validates the response using the GPT-4 model.
+
+        :param question: The yes/no question asked to the worker.
+        :param docs: A list of document(s) uploaded by the worker.
+        :return: VisionMissionResponse containing the validated result or None if an error occurs.
+        """
+        try:
+            docs_text = self._extract_text_from_docs(docs)
+            prompt = validate_worker_prompt()
+            response = self.client.beta.chat.completions.parse(
+                model=self.model,
+                messages=[
+                    {
+                        "role": "system",
+                        "content": f'''{prompt} The document is uploaded next.'''
+                    },
+                    {
+                        "role": "user",
+                        "content": f"Question :{question}",
+                    },
+                    {
+                        "role": "user",
+                        "content": [{"type": "text", "text": text} for text in docs_text],
+                    }
+                ],
+                response_format=ValidateWorker,
+                max_tokens=4096,
+                temperature=0.1
+            )
+
+            # Parse the response from the LLM
+            extracted_text = json.loads(response.choices[0].message.content)
+
+            return extracted_text
+
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return None