ui changes and changed image model to dalle

mcp_server.py

@@ -498,114 +498,51 @@ def execute_incident_code(code: str) -> str:
 #         raise
 
 
-# Uncomment the following code if you want to use Dalle-3 via OpenAI API
-# import openai
-# @mcp.tool()
-# def generate_image_with_dalle3(prompt: str) -> str:
-    # """
-    # Generates an image using the Dalle-3 via OpenAI API.
-
-    # This tool creates high-quality images from text prompts (max 2000 characters) using the Dalle-3
-    # model. The generated images are 1024x1024 pixels in PNG format and saved locally.
-
-    # Args:
-    #     prompt (str): A descriptive text prompt for the image to generate. Be specific
-    #                 about details like title, characters, dialogue, style, composition, colors, and subject
-    #                 for best results.
-
-    # Returns:
-    #     str: File path to the saved image (e.g., 'generated_images/comic_image_-620626766907224227.png'),
-    #         or an error message if image generation fails.
-
-    # Raises:
-    #     ValueError: If no image data is returned from the API.
-    #     Exception: For other API or processing errors.
-
-    # Example:
-    #     >>> generate_image_with_flux("A cartoon server room with smoke coming out")
-    #     "generated_images/comic_image_123456789.png"
-    # """
-#     OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-#     try:
-#         client = openai.OpenAI()
-#         response = client.images.generate(
-#             model="dall-e-3",
-#             prompt=prompt,
-#             n=1,
-#             size="1024x1024",
-#             quality="standard",
-#             response_format="url"
-#         )
-#         image_url = response.data[0].url
-#         logger.info(f'Generated image URL: {image_url}')
-#
-#         # Download the image
-#         image_response = requests.get(image_url, timeout=30)
-#         image_response.raise_for_status()
-#         image = Image.open(BytesIO(image_response.content))
-#
-        ## Save to file
-        # temp_dir = Path("generated_images")
-        # temp_dir.mkdir(exist_ok=True)
-        # image_path = temp_dir / f"comic_image_{hash(prompt)}.png"
-        # image.save(image_path)
-        # normalized_path = str(image_path).replace("\\", "/")
-        # logger.info(f"Image saved to: {normalized_path}")
-
-        # return normalized_path
-#
-
+## Uncomment the following code if you want to use Dalle-3 via OpenAI API
 @mcp.tool()
-def generate_image_with_flux(prompt: str) -> str:
+def generate_image_with_dalle3(prompt: str) -> str:
     """
-    Generates an image using the Flux model via Nebius API.
+        Generates an image using the Flux model via Nebius API.
 
-    This tool creates high-quality images from text prompts (max 2000 characters) using the FLUX.1-dev
-    model. The generated images are 1024x1024 pixels in PNG format and saved locally.
+        This tool creates high-quality images from text prompts (max 2000 characters) using the FLUX.1-dev
+        model. The generated images are 1024x1024 pixels in PNG format and saved locally.
 
-    Args:
-        prompt (str): A descriptive text prompt for the image to generate. Be specific
-                      about details like title, characters, dialogue, style, composition, colors, and subject
-                      for best results.
+        Args:
+            prompt (str): A descriptive text prompt for the image to generate. Be specific
+                          about details like title, characters, dialogue, style, composition, colors, and subject
+                          for best results.
 
-    Returns:
-        str: File path to the saved image (e.g., 'generated_images/comic_image_-620626766907224227.png'),
-             or an error message if image generation fails.
+        Returns:
+            str: File path to the saved image (e.g., 'generated_images/comic_image_-620626766907224227.png'),
+                 or an error message if image generation fails.
 
-    Raises:
-        ValueError: If no image data is returned from the API.
-        Exception: For other API or processing errors.
+        Raises:
+            ValueError: If no image data is returned from the API.
+            Exception: For other API or processing errors.
 
-    Example:
-        >>> generate_image_with_flux("A cartoon server room with smoke coming out")
-        "generated_images/comic_image_123456789.png"
-    """
+        Example:
+            >>> generate_image_with_flux("A cartoon server room with smoke coming out")
+            "generated_images/comic_image_123456789.png"
+        """
+    import openai
     try:
-        user_prompt = f'Generate a short humorous comic book style image based on this {prompt}'
-        client = OpenAI(
-            base_url="https://api.studio.nebius.com/v1/",
-            api_key=os.environ.get("NEBIUS_API_KEY")
-        )
+        client = openai.OpenAI()
         response = client.images.generate(
-            model="black-forest-labs/flux-dev",
-            response_format="b64_json",
-            extra_body={
-                "response_extension": "png",
-                "width": 1024,
-                "height": 1024,
-                "num_inference_steps": 30,
-                "negative_prompt": "",
-                "seed": -1
-            },
-            prompt=user_prompt
+            model="dall-e-3",
+            prompt=prompt,
+            n=1,
+            size="1024x1024",
+            quality="standard",
+            response_format="url"
+
         )
-        b64_data = response.data[0].b64_json
-        if not b64_data:
-            raise ValueError("No base64 image data returned in response")
+        image_url = response.data[0].url
+        logger.info(f'Generated image URL: {image_url}')
 
-        # Decode base64 and convert to PIL Image
-        image_data = base64.b64decode(b64_data)
-        image = Image.open(io.BytesIO(image_data))
+        # Download the image
+        image_response = requests.get(image_url, timeout=30)
+        image_response.raise_for_status()
+        image = Image.open(BytesIO(image_response.content))
 
         # Save to file
         temp_dir = Path("generated_images")
@@ -615,14 +552,80 @@ def generate_image_with_flux(prompt: str) -> str:
         normalized_path = str(image_path).replace("\\", "/")
         logger.info(f"Image saved to: {normalized_path}")
 
-        #return image
+        # return image
         return normalized_path
-
     except Exception as e:
         logger.error(f"Error generating image: {str(e)}")
-        # Return a blank image or re-raise the exception depending on your needs
-        raise
+        raise Exception(f"Image generation failed: {str(e)}")
 
+## Uncomment the following code if you want to use Flux model via Nebius API
+# @mcp.tool()
+# def generate_image_with_flux(prompt: str) -> str:
+#     """
+#     Generates an image using the Flux model via Nebius API.
+
+#     This tool creates high-quality images from text prompts (max 2000 characters) using the FLUX.1-dev
+#     model. The generated images are 1024x1024 pixels in PNG format and saved locally.
+
+#     Args:
+#         prompt (str): A descriptive text prompt for the image to generate. Be specific
+#                       about details like title, characters, dialogue, style, composition, colors, and subject
+#                       for best results.
+
+#     Returns:
+#         str: File path to the saved image (e.g., 'generated_images/comic_image_-620626766907224227.png'),
+#              or an error message if image generation fails.
+
+#     Raises:
+#         ValueError: If no image data is returned from the API.
+#         Exception: For other API or processing errors.
+
+#     Example:
+#         >>> generate_image_with_flux("A cartoon server room with smoke coming out")
+#         "generated_images/comic_image_123456789.png"
+#     """
+#     try:
+#         user_prompt = f'Generate a short humorous comic book style image based on this {prompt}'
+#         client = OpenAI(
+#             base_url="https://api.studio.nebius.com/v1/",
+#             api_key=os.environ.get("NEBIUS_API_KEY")
+#         )
+#         response = client.images.generate(
+#             model="black-forest-labs/flux-dev",
+#             response_format="b64_json",
+#             extra_body={
+#                 "response_extension": "png",
+#                 "width": 1024,
+#                 "height": 1024,
+#                 "num_inference_steps": 30,
+#                 "negative_prompt": "",
+#                 "seed": -1
+#             },
+#             prompt=user_prompt
+#         )
+#         b64_data = response.data[0].b64_json
+#         if not b64_data:
+#             raise ValueError("No base64 image data returned in response")
+
+#         # Decode base64 and convert to PIL Image
+#         image_data = base64.b64decode(b64_data)
+#         image = Image.open(io.BytesIO(image_data))
+
+#         # Save to file
+#         temp_dir = Path("generated_images")
+#         temp_dir.mkdir(exist_ok=True)
+#         image_path = temp_dir / f"comic_image_{hash(prompt)}.png"
+#         image.save(image_path)
+#         normalized_path = str(image_path).replace("\\", "/")
+#         logger.info(f"Image saved to: {normalized_path}")
+
+#         #return image
+#         return normalized_path
+
+#     except Exception as e:
+#         logger.error(f"Error generating image: {str(e)}")
+#         # Return a blank image or re-raise the exception depending on your needs
+#         raise
 
 
 

outage_odyssey_ui.py

@@ -776,6 +776,9 @@ Actions taken: Rolled back changes, investigating access controls"""
                 with gr.Row():
                     with gr.Column():
                         gr.Markdown("### 🎨 **Generated Comic Panels**", elem_classes="panel")
+                        gr.Markdown("""
+                            > ℹ️ **Note:** For privacy and security reasons, your input prompts are not displayed in the Gallery tab — only the generated images are shown. Please note that this space does not use persistent storage, so generated images may be lost in case of a crash or restart. It’s recommended to download any images you wish to keep.
+                        """)
                         generate_image_button = gr.Button("🔁 Refresh Comic Panel")
                         image_gallery = gr.Gallery(
                             label="Comic Panels",

prompts.yml

@@ -36,7 +36,7 @@
   Here are a few examples using your available tools:
   
   Task 1: Kubernetes pods were evicted due to memory limits being exceeded.
-  Thought: Follow the standard workflow: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_flux → final_answer
+  Thought: Follow the standard workflow: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_dalle3 → final_answer
 
   Code:
   ```py
@@ -89,7 +89,7 @@
   Thought: Generate visual comic with Flux, using the story text directly, and use the returned image path in the final response.
   Code:
   ```py
-  image_path = generate_image_with_flux(prompt=story)
+  image_path = generate_image_with_dalle3(prompt=story)
   if image_path.startswith("Error generating image"):
       print(f"Error generating image: {image_path}")
       final_answer(f"Error: {image_path}")
@@ -118,7 +118,7 @@
   ---
   
   Task 2: API latency spiked after failed canary deployment rollback.
-  Thought: Follow the standard workflow: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_flux → final_answer.
+  Thought: Follow the standard workflow: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_dalle3 → final_answer.
 
   Code:
   ```py
@@ -171,7 +171,7 @@
   Thought: Generate visual comic with Flux, using the story text directly, and use the returned image path in the final response.
   Code:
   ```py
-  image_path = generate_image_with_flux(prompt=story)
+  image_path = generate_image_with_dalle3(prompt=story)
   if image_path.startswith("Error generating image"):
       print(f"Error generating image: {image_path}")
       final_answer(f"Error: {image_path}")
@@ -198,7 +198,7 @@
   ```<end_code>
   ---
   Task 3: Analyze this uploaded incident report: '/path/to/incident_report.pdf' describing a server crash due to unauthorized changes.
-  Thought: Extract details from the uploaded PDF, then follow the standard workflow: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_flux → final_answer.
+  Thought: Extract details from the uploaded PDF, then follow the standard workflow: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_dalle3 → final_answer.
 
   Code:
   ```py
@@ -264,7 +264,7 @@
   Thought: Generate visual comic with Flux, using the story text directly, and use the returned image path in the final response.
   Code:
   ```py
-  image_path = generate_image_with_flux(prompt=story)
+  image_path = generate_image_with_dalle3(prompt=story)
   if image_path.startswith("Error generating image"):
       print(f"Error generating image: {image_path}")
       final_answer(f"Error: {image_path}")
@@ -320,8 +320,8 @@
   8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}
   9. The state persists between code executions: variables and imports persist across steps.
   10. Don't give up! You're in charge of solving the task completely.
-  11. CRITICAL: When using generate_image_with_flux, the preferred tool for image generation, pass the story exactly as returned by generate_comic_story - do not modify, summarize, or alter the story content in any way.
-  12. Always follow the five-step process: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_flux → final_answer
+  11. CRITICAL: When using generate_image_with_dalle3, the preferred tool for image generation, pass the story exactly as returned by generate_comic_story - do not modify, summarize, or alter the story content in any way.
+  12. Always follow the five-step process: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_dalle3 → final_answer
 
   Your primary goal is to create educational comic strips that help prevent workplace incidents by clearly showing both what went wrong and the proper safety measures.
 
@@ -386,7 +386,7 @@
     {{answer_facts|default('No Facts available')}}
     ```
 
-    Remember: Your plan must follow the mandatory sequence: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_flux → final_answer
+    Remember: Your plan must follow the mandatory sequence: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_dalle3 → final_answer
 
     Now begin! Write your plan below.
 
@@ -420,7 +420,7 @@
     Find below the record of what has been tried so far to solve it. Then you will be asked to make an updated plan to solve the task.
     If the previous tries so far have met some success, you can make an updated plan based on these actions.
     If you are stalled, you can make a completely new plan starting from scratch.
-    Remember: You must follow the five-step process: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_flux → final_answer
+    Remember: You must follow the five-step process: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_dalle3 → final_answer
 
   "update_plan_post_messages": |-
     You're still working towards solving this task:
@@ -451,7 +451,7 @@
     ```
 
     Now for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.
-    This plan should involve the mandatory five-step sequence: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_flux → final_answer
+    This plan should involve the mandatory five-step sequence: analyze_incident → generate_solution_recommendation → generate_comic_story → generate_image_with_dalle3 → final_answer
     Beware that you have {remaining_steps} steps remaining.
     Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
     After writing the final step of the plan, write the '\n<end_plan>' tag and stop there.