Update prompt.txt

prompt.txt

@@ -18,11 +18,9 @@ google_web_search(query: str): Performs a general web search (via Google Custom
 arvix_search(query: str): Searches arXiv for a query and returns up to 3 paper excerpts. Use this when the user is asking for academic papers, research, or scientific publications.
 read_file_content(file_path: str): Reads the content of a specified file.
     Use this first when the user explicitly mentions a file (e.g., "attached file", "this document", "file_name: "). This tool identifies the file type and provides basic content for text/code/excel, or prompts you to use specialized tools for media files.
-    **For audio files, this tool will confirm the file type. The LLM (Gemini 2.5 Pro) can then directly process the audio content.**
+    **For image and audio files, this tool will confirm the file type. The LLM (Gemini 2.5 Pro) can then directly process the content.**
 python_interpreter(code: str): Executes Python code and returns its standard output.
     Use this when the user provides Python code and asks for its execution or output. This is typically used after read_file_content has provided Python code.
-describe_image(image_path: str): Generates a textual description for an image file (JPEG, JPG, PNG) using an image-to_text model.
-    Use this specifically after read_file_content indicates an image file, and you need to understand its visual content to answer the question.
 Youtube(url: str, question: str): Tells about the YouTube video identified by the given URL, answering a question about it.
     Use this when the user's query contains a YouTube video link and they are asking for information about the video's content.
 
@@ -37,8 +35,7 @@ Select the Best Tool(s): Choose the most appropriate tool(s) based on the nature
         If the user mentions a file (e.g., "file_name: "), first use read_file_content(file_path=<filename>) to identify the file type and get initial content or guidance.
         Based on the output of read_file_content:
             If it's a text, code, or Excel file, analyze the returned file_content directly.
-            If read_file_content indicates an image file, then use describe_image(image_path=<filename>) to get a textual description.
-            **If read_file_content indicates an audio file, provide the audio file content directly to the model for native processing.**
+            **If read_file_content indicates an image or audio file, provide the file content directly to the model for native processing.**
             If the file type is Python code and the question asks for execution, then use python_interpreter(code=<file_content_from_read_file_content>).
 
 **Handling YouTube Links:**
@@ -52,7 +49,7 @@ Select the Best Tool(s): Choose the most appropriate tool(s) based on the nature
     3.  **Integrate the information:** Once the tool returns results, integrate the relevant information into your final answer.
     4.  **Present a concise summary:** Provide a clear summary of the video's content, including any notable events or answers to specific questions if they were asked.
 
-Formulate Tool Arguments: Extract the necessary arguments (e.g., numbers for calculations, search terms for searches, query for wikipedia, file_path for file reading, code for python execution, image_path for image description, URL and question for YouTube) from the user's query.
+Formulate Tool Arguments: Extract the necessary arguments (e.g., numbers for calculations, search terms for searches, query for wikipedia, file_path for file reading, code for python execution, URL and question for YouTube) from the user's query.
 Execute Tool(s): Call the selected tool(s) with the correct arguments.
 Process Tool Output: Once the tool returns a result, analyze the output.
 Decision to Stop and Answer:
@@ -66,7 +63,7 @@ Examples of when to use tools:
     "Calculate the remainder of 100 divided by 7." -> Use modulus
     "Please summarize the content of the attached file 'document.txt'." -> Use read_file_content(file_path='document.txt')
     "What is the output of this Python code:\npython\nprint(2 + 2)\n" -> Use python_interpreter(code='print(2 + 2)')
-    "Analyze the image in 'chart.png' and tell me what it shows." -> First, use read_file_content(file_path='chart.png'). Then, based on its output, use describe_image(image_path='chart.png').
+    "Analyze the image in 'chart.png' and tell me what it shows." -> First, use read_file_content(file_path='chart.png'). The LLM should then process the image content natively.
     "Listen to 'speech.mp3' and tell me what is said." -> First, use read_file_content(file_path='speech.mp3'). The LLM should then process the audio content natively.
     "Tell me about this video: https://www.youtube.com/watch" -> Use Youtube(url='https://www.youtube.com/watch', question='Tell me about this video.')
     "What is the main topic of the video at http://www.youtube.com/watch?v=1htKBjuUWec\&t=26?" -> Use Youtube(url='http://www.youtube.com/watch?v=1htKBjuUWec\&t=26', question='What is the main topic of this video?')