Update langgraph_agent.py

langgraph_agent.py

@@ -92,34 +92,42 @@ if HF_API_TOKEN:
 else:
     print("WARNING: HF_API_TOKEN not set. If any other HF tools are used, they might not function.")
 
-@tool
+@tooldef
 def read_file_content(file_path: str) -> Dict[str, str]:
-    """Reads the content of a file and returns its primary information. For text/code/excel, returns content. For media, returns a prompt to use specific tools."""
+    """Reads the content of a file and returns its primary information. For text/code/excel, returns content. For media, indicates it's a blob for LLM processing."""
     try:
         _, file_extension = os.path.splitext(file_path)
         file_extension = file_extension.lower()
 
-        if file_extension in (".txt", ".py"):
+        # Prioritize handling of video, audio, and image files for direct LLM processing
+        if file_extension in (".mp4", ".avi", ".mov", ".mkv", ".webm"):
+            return {"file_type": "video", "file_name": file_path, "file_content": f"Video file '{file_path}' detected. The LLM (Gemini 2.5 Pro) can process this video content directly as a blob."}
+        elif file_extension == ".mp3":
+            return {"file_type": "audio", "file_name": file_path, "file_content": f"Audio file '{file_path}' detected. The LLM (Gemini 2.5 Pro) can process this audio content directly as a blob."}
+        elif file_extension in (".jpeg", ".jpg", ".png"):
+            return {"file_type": "image", "file_name": file_path, "file_content": f"Image file '{file_path}' detected. The LLM (Gemini 2.5 Pro) can process this image content directly as a blob."}
+        
+        # Handle text and code files
+        elif file_extension in (".txt", ".py"):
             with open(file_path, "r", encoding="utf-8") as f:
                 content = f.read()
             return {"file_type": "text/code", "file_name": file_path, "file_content": content}
+        
+        # Handle Excel files
         elif file_extension == ".xlsx":
             df = pd.read_excel(file_path)
             content = df.to_string()
             return {"file_type": "excel", "file_name": file_path, "file_content": content}
-        elif file_extension in (".jpeg", ".jpg", ".png"):
-            # For images, we indicate it's an image file and expect the LLM to handle the blob directly.
-            return {"file_type": "image", "file_name": file_path, "file_content": f"Image file '{file_path}' detected. The LLM (Gemini 2.5 Pro) can process this image content directly."}
-        elif file_extension == ".mp3":
-            # For MP3, we indicate it's an audio file and expect the LLM to handle the blob directly.
-            return {"file_type": "audio", "file_name": file_path, "file_content": f"Audio file '{file_path}' detected. The LLM (Gemini 2.5 Pro) can process this audio content directly."}
+        
         else:
-            return {"file_type": "unsupported", "file_name": file_path, "file_content": f"Unsupported file type: {file_extension}. Only .txt, .py, .xlsx, .jpeg, .jpg, .png, .mp3 files are recognized."}
+            return {"file_type": "unsupported", "file_name": file_path, "file_content": f"Unsupported file type: {file_extension}. Only .txt, .py, .xlsx, .jpeg, .jpg, .png, .mp3, .mp4, .avi, .mov, .mkv, .webm files are recognized."}
+    
     except FileNotFoundError:
         return {"file_error": f"File not found: {file_path}. Please ensure the file exists in the environment."}
     except Exception as e:
         return {"file_error": f"Error reading file {file_path}: {e}"}
 
+
 @tool
 def python_interpreter(code: str) -> Dict[str, str]:
     """Executes Python code and returns its standard output. If there's an error during execution, it returns the error message."""