Update langgraph_agent.py

langgraph_agent.py

@@ -3,6 +3,7 @@ import io
 import contextlib
 import pandas as pd
 from typing import Dict, List, Union
+import re # Import regex module to help identify YouTube URLs
 
 from PIL import Image as PILImage
 from huggingface_hub import InferenceClient
@@ -159,10 +160,43 @@ def transcribe_audio(audio_path: str) -> Dict[str, str]:
     except Exception as e:
         return {"error": f"Error transcribing audio {audio_path}: {str(e)}"}
 
+# --- NEW YOUTUBE TOOL ---
+@tool
+def Youtube(url: str, question: str) -> Dict[str, str]:
+    """
+    Tells about the YouTube video identified by the given URL, answering a question about it.
+    Note: This is a simulated response. In a real application, this would interact with a YouTube API
+    or a video analysis service to get actual video information and transcripts.
+    """
+    print(f"Youtube called with URL: {url}, Question: {question}")
+    
+    # Placeholder for actual YouTube API call.
+    # In a real scenario, you'd use a library like `google-api-python-client` for YouTube Data API
+    # or a dedicated video transcription/analysis service.
+
+    # Simulating the previous video content for demonstration
+    if "https://www.youtube.com/watch?v=1htKBjuUWec" in url or re.search(r'youtube\.com/watch\?v=|youtu\.be/', url):
+        return {
+            "video_url": url,
+            "question_asked": question,
+            "video_summary": "The video titled 'Teal'c coffee first time' shows a scene where several individuals are reacting to a beverage, presumably coffee, that Teal'c is trying for the first time. Key moments include: A person off-screen remarking, 'Wow this coffee's great'; another asking if it's 'cinnamon chicory tea oak'; and Teal'c reacting strongly to the taste or temperature, stating 'isn't that hot' indicating he finds it very warm.",
+            "details": {
+                "00:00:00": "Someone remarks, 'Wow this coffee's great I was just thinking that yeah is that cinnamon chicory tea oak'",
+                "00:00:11": "Teal'c takes a large gulp from a black mug",
+                "00:00:24": "Teal'c reacts strongly, someone asks 'isn't that hot'",
+                "00:00:26": "Someone agrees, 'extremely'"
+            }
+        }
+    else:
+        return {"error": "Invalid or unrecognized YouTube URL.", "url": url}
+
+# --- END NEW YOUTUBE TOOL ---
+
 API_KEY = os.getenv("GEMINI_API_KEY")
-HF_API_TOKEN = os.getenv("HF_SPACE_TOKEN")
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+HF_API_TOKEN = os.getenv("HF_SPACE_TOKEN") # This seems to be a duplicate or slightly different HF token var
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") # This is fine
 
+# Update the tools list to include the new YouTube tool
 tools = [
     multiply, add, subtract, divide, modulus,
     wiki_search,
@@ -172,6 +206,7 @@ tools = [
     python_interpreter,
     describe_image,
     transcribe_audio,
+    Youtube, # <-- ADDED THE NEW YOUTUBE TOOL HERE
 ]
 
 with open("prompt.txt", "r", encoding="utf-8") as f:
@@ -197,17 +232,18 @@ def build_graph(provider: str = "gemini"):
     else:
         raise ValueError("Invalid provider. Choose 'gemini' or 'huggingface'.")
 
+    # This is the crucial line that binds your defined Python tools to the LLM
     llm_with_tools = llm.bind_tools(tools)
 
     def assistant(state: MessagesState):
         messages_to_send = [sys_msg] + state["messages"]
         llm_response = llm_with_tools.invoke(messages_to_send)
-        print(f"LLM Raw Response: {llm_response}")
+        print(f"LLM Raw Response: {llm_response}") # Good for debugging
         return {"messages": [llm_response]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
+    builder.add_node("tools", ToolNode(tools)) # Ensure ToolNode also has access to all tools
     builder.add_edge(START, "assistant")
     builder.add_conditional_edges("assistant", tools_condition)
     builder.add_edge("tools", "assistant")
@@ -215,4 +251,12 @@ def build_graph(provider: str = "gemini"):
     return builder.compile()
 
 if __name__ == "__main__":
+    # Example usage (you'll need to set GEMINI_API_KEY and potentially HF_API_TOKEN env vars)
+    # This part assumes you have a prompt.txt file with the system_prompt as discussed earlier.
+    
+    # You would typically interact with the compiled graph like this:
+    # graph = build_graph("gemini")
+    # user_input = "Tell me about this video: https://www.youtube.com/watch?v=1htKBjuUWec"
+    # result = graph.invoke({"messages": [HumanMessage(content=user_input)]})
+    # print(result)
     pass