Update customtools.py

customtools.py

@@ -20,9 +20,14 @@ from langchain_community.document_loaders import WikipediaLoader
 from langchain_openrouter import ChatOpenRouter
 from tavily import TavilyClient
 from youtube_transcript_api import YouTubeTranscriptApi
+from langchain_nvidia_ai_endpoints import ChatNVIDIA
+global llm
 
 from config import (
     OPENROUTER_API_KEY,
+    NVIDIA_MODEL,
+    NVIDIA,
+    NVIDIA_API_KEY,
     TAVILY_API_KEY,
     LLM_MODEL,
     LLM_TEMPERATURE,
@@ -53,17 +58,21 @@ def wikisearch(query: str, max_pages: int = None) -> str:
 
 
 @tool
-def youtube_transcript(url: str, chars: int = None) -> str:
-    """Fetch YouTube video transcript."""
-    chars = chars or YOUTUBE_CHAR_LIMIT
+def youtube_transcript(url: str) -> str:
+    """Fetch YouTube video transcript from given URL."""
+    chars = YOUTUBE_CHAR_LIMIT
     video_id_match = re.search(r"[?&]v=([A-Za-z0-9_\-]{11})", url)
-    
+    #print(f"Video is {video_id_match.group(1)}")
+    video_id = None
     if not video_id_match:
         return "Error: Could not extract video ID from URL"
-    
+    else:
+        video_id=video_id_match.group(1)
+    #print(YouTubeTranscriptApi.__getattribute__())
+    print(video_id)
     try:
-        transcript = YouTubeTranscriptApi.get_transcript(video_id_match.group(1))
-        text = " ".join(piece["text"] for piece in transcript)
+        transcript = YouTubeTranscriptApi().fetch(video_id)
+        text = " ".join([piece.text] for piece in transcript)
         return text[:chars]
     except Exception as exc:
         print(f"Error fetching YouTube transcript: {exc}")
@@ -154,6 +163,16 @@ def division_tool(a: str, b: str) -> str:
 
 
 
+@tool
+def modulus_tool(a: int, b: int) -> int:
+    """Get the modulus of two numbers.
+    
+    Args:
+        a: first int
+        b: second int
+    """
+    return a % b
+
 
 @tool
 def extract_text_from_image(image_path: str) -> str:
@@ -228,13 +247,22 @@ def load_and_analyze_excel_file(query: str, file_path: str) -> str:
             data_summary=data_summary,
             query=query
         )
-        
-        # Get LLM analysis
-        tool_llm = ChatOpenRouter(
-            model=LLM_MODEL,
-            temperature=LLM_TEMPERATURE,
-            api_key=OPENROUTER_API_KEY,
-        )
+        tool_llm=None
+        if NVIDIA:
+            tool_llm = ChatNVIDIA(
+                                model=NVIDIA_MODEL,
+                                api_key= NVIDIA_API_KEY, 
+                                temperature=1,
+                                top_p=1,
+                                
+            )
+        else:        
+            # Get LLM analysis
+            tool_llm = ChatOpenRouter(
+                model=LLM_MODEL,
+                temperature=LLM_TEMPERATURE,
+                api_key=OPENROUTER_API_KEY,
+            )
         
         message = HumanMessage(content=analysis_prompt)
         llm_response = tool_llm.invoke([message])
@@ -252,7 +280,7 @@ def load_and_analyze_excel_file(query: str, file_path: str) -> str:
 def transcribe_audio(audio_file: str) -> str:
     """Transcribe audio files and return the transcript."""
     try:
-        model = whisper.load_model("base")
+        model = whisper.load_model("small")
         output = model.transcribe(audio=str(Path(audio_file)), language='en')
         print(f"Audio transcription completed")
         return output['text']