Create utils.py

utils.py

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+import base64
+import io
+import logging
+from typing import Optional, Any 
+from PIL import Image 
+from openai import OpenAI
+
+logging.basicConfig(level=logging.INFO) 
+
+class CommonUtils:
+    
+    @staticmethod
+    def convert_image_to_base64(image: Image.Image) -> str:
+        """Converts the image to base64."""
+        buffered = io.BytesIO()
+        image.save(buffered, format="JPEG")
+        img_bytes = buffered.getvalue()
+        return base64.b64encode(img_bytes).decode('utf-8')
+
+    @staticmethod
+    def validate_api_key(api_key: str) -> bool:
+        """Validates that the API key is provided and not empty."""
+        return bool(api_key and api_key.strip())
+
+    @staticmethod
+    def validate_image(image: Optional[Image.Image]) -> bool:
+        """Validates that an image is provided."""
+        return image is not None
+
+    @staticmethod
+    def call_mistral_vision_api(api_key: str, model_name: str, prompt: str, base64_image: str) -> str:
+        try:
+            client = OpenAI(base_url="https://api.studio.nebius.com/v1/", api_key=api_key)
+            chat_completion = client.chat.completions.create(
+                messages=[{
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}},
+                    ],
+                }],
+                model=model_name
+            )
+            return chat_completion.choices[0].message.content or "Error: No content returned"
+        except Exception as e:
+            error_message = f"Error calling Nebius Vision API: {str(e)}"
+            logging.error(error_message)
+            return error_message
+
+    @staticmethod
+    def create_story_prompt() -> str:
+
+        ret_str =  f"""You are an expert storyteller. Based on the provided content, create a compelling story that captures the essence of the material.
+        1. Focus on the main themes and key points.
+        2. Use vivid descriptions and engaging language to bring the story to life.
+        3. Ensure the story is coherent and flows logically from one point to the next.
+        4. If the content is too short, expand on the themes and add relevant details to enrich the narrative.
+        5. If the content is too long, summarize the key points while maintaining the story's essence.
+        6. If the content is ambiguous, state your assumptions clearly.
+        7. If the content is not suitable for storytelling, provide a brief explanation of why it cannot be transformed into a story.
+        8. If the content is suitable for storytelling, present the story in a clear, engaging format.
+        IMPORTANT: After the main story, include a section clearly marked with **Notes**: that summarizes the key points from the story, and another section marked with **QnA**: that contains questions and answers related to the story. 
+        Example format:
+        **Story**: [Your story here]
+        **Notes**: [Key points from the story]
+        **QnA**: [Questions and answers related to the story]"""
+        return ret_str
+
+    @staticmethod
+    def process_story_teller(api_key:str, image_data: Any) -> str:
+        """Processes the story teller query and returns a response."""
+        try:
+ 
+            logging.info(f"[process_story_teller]")
+
+            if not CommonUtils.validate_api_key(api_key):
+                return "Invalid API key. Please check your credentials." 
+            
+            if not CommonUtils.validate_image(image_data):
+                return "Please upload image first."
+            else:
+                base64_image = CommonUtils.convert_image_to_base64(image_data)
+                vision_model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+                prompt = CommonUtils.create_story_prompt()
+                response = CommonUtils.call_mistral_vision_api(api_key, vision_model_name, prompt, base64_image)
+
+                logging.info(f"Raw AI response: {response}")
+
+                if not response:
+                    return "No response received from the API. Please try again later."  
+
+                ai_response = response.strip() 
+
+                if ai_response.lower().startswith("error"):
+                    return ("An error occurred while processing your story. Please check the API key and try again.", "", "")
+                 
+                notes_idx = ai_response.find("**Notes")
+                qna_idx = ai_response.find("**QnA")
+                main = ai_response[:notes_idx].replace("**Story**:", "").replace("**","").strip()
+                notes_data = ai_response[notes_idx:qna_idx].replace("**Notes**:", "").replace("**","").strip()
+                qna_data = ai_response[qna_idx:].replace("**QnA**:", "").replace("**","").strip()
+                return (main, notes_data, qna_data)
+            
+        except Exception as e:
+            print(f"[process_story_teller ERROR] {e}")
+            return "An error occurred while processing your story." 
+        
+    @staticmethod
+    def clear_outputs():
+        """Clear all outputs"""
+        return ("🔄 Cleared All - Ready for new Story", "", "", "")