Delete chroma.sqlite3

Update .gitignore

updates

Update .gitignore

Update .gitignore

.gitignore

@@ -1,2 +1,4 @@
 .env
 .venv
+chroma_db/chroma.sqlite3
+__pycache__

agent.py

@@ -139,12 +139,12 @@ def build_graph():
 
     # --- Graph Definition ---
     builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever_node)
+    # builder.add_node("retriever", retriever_node)
     builder.add_node("assistant", assistant)
     builder.add_node("tools", ToolNode(tools))
 
-    builder.add_edge(START, "retriever")
-    builder.add_edge("retriever", "assistant")
+    builder.add_edge(START, "assistant")
+    # builder.add_edge("retriever", "assistant")
     builder.add_conditional_edges("assistant", tools_condition)
     builder.add_edge("tools", "assistant")
 

tools.py

@@ -766,13 +766,20 @@ def reverse_sentence(text: str) -> str:
     return text[::-1]
 
 
-def get_max_bird_species_count_from_video(url: str) -> Dict:
+@tool
+def get_max_bird_species_count_from_video(
+    url: str,
+    model_path: str = "best_birds.pt",
+    sample_rate_seconds: int = 1
+) -> dict:
     """
     Downloads a YouTube video and returns the maximum number of unique bird species 
-    visible in any frame, along with the timestamp.
+    visible in any frame, along with the timestamp and species list.
 
-    Parameters:
-        url (str): YouTube video URL
+    Args:
+        url (str): YouTube video URL.
+        model_path (str): Path to the YOLOv5 bird species detection model.
+        sample_rate_seconds (int): How often (in seconds) to sample frames.
 
     Returns:
         dict: {
@@ -781,50 +788,60 @@ def get_max_bird_species_count_from_video(url: str) -> Dict:
             "species_list": List[str],
         }
     """
-    # 1. Download YouTube video
-    yt = YouTube(url)
-    stream = yt.streams.filter(file_extension='mp4').get_highest_resolution()
+    import traceback
+
     temp_video_path = os.path.join(tempfile.gettempdir(), "video.mp4")
-    stream.download(filename=temp_video_path)
-
-    # 2. Load object detection model for bird species
-    # Load a fine-tuned YOLOv5 model or similar pretrained on bird species
-    model = torch.hub.load('ultralytics/yolov5', 'custom', path='best_birds.pt')  # path to your trained model
-
-    # 3. Process video frames
-    cap = cv2.VideoCapture(temp_video_path)
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    frame_interval = int(fps * 1)  # 1 frame per second
-
-    max_species_count = 0
-    max_species_frame_time = 0
-    species_at_max = []
-
-    frame_idx = 0
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        if frame_idx % frame_interval == 0:
-            # Run detection
-            results = model(frame)
-            detected_species = set()
-            for *box, conf, cls in results.xyxy[0]:
-                species_name = model.names[int(cls)]
-                detected_species.add(species_name)
-
-            if len(detected_species) > max_species_count:
-                max_species_count = len(detected_species)
-                max_species_frame_time = int(cap.get(cv2.CAP_PROP_POS_MSEC)) // 1000
-                species_at_max = list(detected_species)
-
-        frame_idx += 1
-
-    cap.release()
-    os.remove(temp_video_path)
-
-    return {
-        "max_species_count": max_species_count,
-        "timestamp": f"{max_species_frame_time}s",
-        "species_list": species_at_max
-    }
+    try:
+        # Download YouTube video
+        yt = YouTube(url)
+        stream = yt.streams.filter(file_extension='mp4').get_highest_resolution()
+        stream.download(filename=temp_video_path)
+
+        # Load object detection model
+        model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path)
+
+        # Process video frames
+        cap = cv2.VideoCapture(temp_video_path)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        if not fps or fps <= 0:
+            return {"error": "Could not determine video FPS."}
+        frame_interval = int(fps * sample_rate_seconds)
+
+        max_species_count = 0
+        max_species_frame_time = 0
+        species_at_max = []
+
+        frame_idx = 0
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            if frame_idx % frame_interval == 0:
+                # Run detection
+                results = model(frame)
+                detected_species = set()
+                for *box, conf, cls in results.xyxy[0]:
+                    species_name = model.names[int(cls)]
+                    detected_species.add(species_name)
+
+                if len(detected_species) > max_species_count:
+                    max_species_count = len(detected_species)
+                    max_species_frame_time = int(cap.get(cv2.CAP_PROP_POS_MSEC)) // 1000
+                    species_at_max = list(detected_species)
+
+            frame_idx += 1
+
+        cap.release()
+        return {
+            "max_species_count": max_species_count,
+            "timestamp": f"{max_species_frame_time}s",
+            "species_list": species_at_max
+        }
+    except Exception as e:
+        return {"error": f"Exception occurred: {str(e)}\n{traceback.format_exc()}"}
+    finally:
+        if os.path.exists(temp_video_path):
+            try:
+                os.remove(temp_video_path)
+            except Exception:
+                pass