Sync from GitHub to Hugging Face

requirements.txt

@@ -12,3 +12,5 @@ datasets
 
 nbformat
 nbconvert
+
+ipython

space_repo/space_repo/app.py

@@ -52,12 +52,36 @@ for cell in nb.cells:
         cell.source = "\n".join(lines)
 
 
-# --- Export cleaned notebook to Python ---
+# --- Export cleaned notebook to Python (via nbformat export) ---
 py_exporter = PythonExporter()
 (code, _) = py_exporter.from_notebook_node(nb)
+
+# write initial converted file
 with open("yolo_converted.py", "w") as f:
     f.write(code)
 
+# --- Post-process the generated file to fix indentation issues from removed lines ---
+import re
+
+with open("yolo_converted.py", "r") as f:
+    conv_code = f.read()
+
+# 1) Replace any lines that are only indented 'pass  # skipped during conversion'
+#    with an unindented version so they don't break top-level structure.
+conv_code = re.sub(r'^[ \t]+pass  # skipped during conversion\s*$', 'pass  # skipped during conversion\n', conv_code, flags=re.M)
+
+# 2) If any 'pass  # skipped during conversion' directly follows a top-level statement
+#    with incorrect indentation, keep them as 'pass' but ensure indentation matches previous block.
+#    (This is conservative; we only normalize leading whitespace for the placeholder)
+#    Already handled by the regex above.
+
+# 3) Remove any leading 'pass  # skipped...' at the very top of the file (if present)
+conv_code = re.sub(r'^\s*pass  # skipped during conversion\s*', '', conv_code, count=1, flags=re.M)
+
+# Save cleaned code back
+with open("yolo_converted.py", "w") as f:
+    f.write(conv_code)
+
 # --- Run the converted YOLO script ---
 mod = runpy.run_path("yolo_converted.py")
 detect_and_classify = mod.get("detect_and_classify")

space_repo/space_repo/space_repo/space_repo/app.py

@@ -29,28 +29,29 @@ with open("YOLO.ipynb") as f:
     nb = nbformat.read(f, as_version=4)
 
 # Remove or skip Google Colab imports and magic commands (! or %) or google colab file picker
-# --- Patch the YOLO notebook code to skip testing lines ---
+# --- Patch the YOLO notebook code to skip testing lines safely ---
 for cell in nb.cells:
     if cell.cell_type == "code":
         lines = []
         for line in cell.source.splitlines():
-                  if (
-                line.strip().startswith("!") or
-                line.strip().startswith("%") or
-                "google.colab" in line or
-                "files.upload" in line or
-                "uploaded" in line or
-                "img_path" in line or                 
-                "detect_and_classify(" in line or         # skip auto test calls
-                "print(" in line or                       # skip print-only outputs
-                "display(" in line                        # skip Jupyter displays
-            ):
+            bad_patterns = [
+                "!", "%",
+                "google.colab",
+                "files.upload",
+                "uploaded",
+                "img_path",
+                "detect_and_classify(",
+                "print(",
+                "display("
+            ]
+            if any(p in line for p in bad_patterns):
+                # Keep Python structure valid (avoid empty if-blocks)
+                lines.append("    pass  # skipped during conversion")
                 continue
             lines.append(line)
         cell.source = "\n".join(lines)
 
 
-
 # --- Export cleaned notebook to Python ---
 py_exporter = PythonExporter()
 (code, _) = py_exporter.from_notebook_node(nb)

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py

@@ -34,14 +34,16 @@ for cell in nb.cells:
     if cell.cell_type == "code":
         lines = []
         for line in cell.source.splitlines():
-            if (
+                  if (
                 line.strip().startswith("!") or
                 line.strip().startswith("%") or
                 "google.colab" in line or
                 "files.upload" in line or
                 "uploaded" in line or
-                "img_path" in line or              # 👈 Added this
-                "detect_and_classify(" in line     # 👈 Skip test calls
+                "img_path" in line or                 
+                "detect_and_classify(" in line or         # skip auto test calls
+                "print(" in line or                       # skip print-only outputs
+                "display(" in line                        # skip Jupyter displays
             ):
                 continue
             lines.append(line)

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py

@@ -29,6 +29,7 @@ with open("YOLO.ipynb") as f:
     nb = nbformat.read(f, as_version=4)
 
 # Remove or skip Google Colab imports and magic commands (! or %) or google colab file picker
+# --- Patch the YOLO notebook code to skip testing lines ---
 for cell in nb.cells:
     if cell.cell_type == "code":
         lines = []
@@ -38,13 +39,16 @@ for cell in nb.cells:
                 line.strip().startswith("%") or
                 "google.colab" in line or
                 "files.upload" in line or
-                  "uploaded" in line  # new line from google colab affecting huggingface setup
+                "uploaded" in line or
+                "img_path" in line or              # 👈 Added this
+                "detect_and_classify(" in line     # 👈 Skip test calls
             ):
                 continue
             lines.append(line)
         cell.source = "\n".join(lines)
 
 
+
 # --- Export cleaned notebook to Python ---
 py_exporter = PythonExporter()
 (code, _) = py_exporter.from_notebook_node(nb)

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py

@@ -37,7 +37,8 @@ for cell in nb.cells:
                 line.strip().startswith("!") or
                 line.strip().startswith("%") or
                 "google.colab" in line or
-                "files.upload" in line
+                "files.upload" in line or
+                  "uploaded" in line  # new line from google colab affecting huggingface setup
             ):
                 continue
             lines.append(line)

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py

@@ -8,6 +8,9 @@ Original file is located at
 """
 
 import os
+os.system("pip install seaborn --quiet")
+
+
 import json
 import torch
 import gradio as gr
@@ -25,15 +28,21 @@ if not os.path.exists("YOLO.ipynb"):
 with open("YOLO.ipynb") as f:
     nb = nbformat.read(f, as_version=4)
 
-# --- Clean notebook magic commands (!pip, !git, %cd, etc.) ---
+# Remove or skip Google Colab imports and magic commands (! or %) or google colab file picker
 for cell in nb.cells:
     if cell.cell_type == "code":
-        cleaned_lines = []
+        lines = []
         for line in cell.source.splitlines():
-            if line.strip().startswith(("!", "%")):
+            if (
+                line.strip().startswith("!") or
+                line.strip().startswith("%") or
+                "google.colab" in line or
+                "files.upload" in line
+            ):
                 continue
-            cleaned_lines.append(line)
-        cell.source = "\n".join(cleaned_lines)
+            lines.append(line)
+        cell.source = "\n".join(lines)
+
 
 # --- Export cleaned notebook to Python ---
 py_exporter = PythonExporter()

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py

@@ -21,14 +21,27 @@ from datasets import load_dataset
 if not os.path.exists("YOLO.ipynb"):
     raise FileNotFoundError("YOLO.ipynb not found in app directory!")
 
-# Convert YOLO.ipynb → yolo_converted.py
+# Read YOLO.ipynb
 with open("YOLO.ipynb") as f:
     nb = nbformat.read(f, as_version=4)
+
+# --- Clean notebook magic commands (!pip, !git, %cd, etc.) ---
+for cell in nb.cells:
+    if cell.cell_type == "code":
+        cleaned_lines = []
+        for line in cell.source.splitlines():
+            if line.strip().startswith(("!", "%")):
+                continue
+            cleaned_lines.append(line)
+        cell.source = "\n".join(cleaned_lines)
+
+# --- Export cleaned notebook to Python ---
 py_exporter = PythonExporter()
 (code, _) = py_exporter.from_notebook_node(nb)
 with open("yolo_converted.py", "w") as f:
     f.write(code)
 
+# --- Run the converted YOLO script ---
 mod = runpy.run_path("yolo_converted.py")
 detect_and_classify = mod.get("detect_and_classify")
 if not detect_and_classify:

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt

@@ -9,3 +9,6 @@ opencv-python
 timm
 transformers
 datasets
+
+nbformat
+nbconvert

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py

@@ -7,146 +7,75 @@ Original file is located at
     https://colab.research.google.com/drive/1gTrf304mzjGMheD47oHDhnYTIrEyf4qp
 """
 
+import os
+import json
+import torch
 import gradio as gr
 from PIL import Image
-import torch
-import os
-
-import os
-from google.colab import files
-
-if not os.path.exists('YOLO.ipynb'):
-    print("Please upload YOLO.ipynb (the script exported from your YOLO notebook).")
-    uploaded = files.upload()  # upload YOLO.ipynb
-    print("Uploaded:", list(uploaded.keys()))
-else:
-    print("YOLO.ipynb already present.")
-
-!ls /content
-
 import nbformat
 from nbconvert import PythonExporter
 import runpy
+from datasets import load_dataset
 
-# Convert YOLO.ipynb to a .py script dynamically
+# --- Convert YOLO notebook to Python ---
+if not os.path.exists("YOLO.ipynb"):
+    raise FileNotFoundError("YOLO.ipynb not found in app directory!")
+
+# Convert YOLO.ipynb → yolo_converted.py
 with open("YOLO.ipynb") as f:
     nb = nbformat.read(f, as_version=4)
-
-# Find the cell that loads car_classifier.pth and modify the path
-# Also, remove any code that tries to open the notebook file as an image
-modified_cells = []
-for cell in nb.cells:
-    if cell.cell_type == 'code':
-        # This is a heuristic: look for lines containing 'car_classifier.pth'
-        if 'car_classifier.pth' in cell.source:
-            cell.source = cell.source.replace("'car_classifier.pth'", "'/content/car_classifier.pth'")
-            cell.source = cell.source.replace('"car_classifier.pth"', '"/content/car_classifier.pth"')
-
-        # Heuristic to remove code that might try to open the notebook as an image
-        if 'Image.open(' in cell.source and 'YOLO.ipynb' in cell.source:
-             cell.source = '# Removed potential image loading of notebook file:\n#' + cell.source
-
 py_exporter = PythonExporter()
 (code, _) = py_exporter.from_notebook_node(nb)
-
-# Save temporarily as script
 with open("yolo_converted.py", "w") as f:
     f.write(code)
 
-# Now safely import detect_and_classify() from that converted script
 mod = runpy.run_path("yolo_converted.py")
 detect_and_classify = mod.get("detect_and_classify")
-
 if not detect_and_classify:
-    raise RuntimeError("Function detect_and_classify not found in YOLO.ipynb")
+    raise RuntimeError("detect_and_classify() not found in YOLO.ipynb")
 
-print("✅ YOLO function imported successfully")
+print("✅ YOLO pipeline loaded successfully")
 
-import torch, os, json
-pth = "/content/car_classifier.pth"
-print("Exists:", os.path.exists(pth))
-ckpt = torch.load(pth, map_location="cpu")
-print("Type:", type(ckpt))
-
-if isinstance(ckpt, dict):
-    keys = list(ckpt.keys())
-    print("Checkpoint keys (first 20):", keys[:20])
-    # If it's a pure state_dict, it will look like parameter names (e.g. 'conv1.weight')
-    # If it's a wrapped checkpoint, it may contain 'model_state_dict' or 'class_names'
-else:
-    print("Checkpoint is not a dict; it's probably a raw model object.")
-
-!pip install -q datasets
-
-from datasets import load_dataset
-ds = load_dataset("tanganke/stanford_cars")
-# HF dataset provides label names in the train feature
-class_names = ds["train"].features["label"].names
-print("Loaded", len(class_names), "class names. Sample:", class_names[:10])
-
-# Save to disk for reuse
-import json
-with open("class_names.json", "w") as f:
-    json.dump(class_names, f, indent=2)
-print("Saved class_names.json")
-
-import json, os
-if os.path.exists("class_names.json"):
-    with open("class_names.json") as f:
-        class_names = json.load(f)
-    print("Loaded class_names from file, len =", len(class_names))
-else:
-    print("class_names.json not found; run the HF cell above.")
-
-import gradio as gr
-import os
-
-# ensure class_names exists in the notebook (from previous cell)
+# --- Load class names ---
 try:
-    assert class_names is not None and len(class_names) > 0
-    print("Using class_names with", len(class_names), "entries")
-except Exception:
+    ds = load_dataset("tanganke/stanford_cars")
+    class_names = ds["train"].features["label"].names
+    with open("class_names.json", "w") as f:
+        json.dump(class_names, f)
+except Exception as e:
+    print("Warning: Could not load dataset class names.", e)
     class_names = None
-    print("class_names not available; will show numeric labels")
 
-def gradio_interface(image, *args, **kwargs):
+# --- Gradio UI ---
+def gradio_interface(image):
     if image is None:
         return "Please upload an image."
-
     temp_path = "temp_image.png"
     image.save(temp_path)
 
     try:
-        results = detect_and_classify(temp_path)  # your notebook function
+        results = detect_and_classify(temp_path)
     except Exception as e:
         return f"❌ Error running YOLO pipeline: {e}"
     finally:
-        if os.path.exists(temp_path):
-            os.remove(temp_path)
+        os.remove(temp_path)
 
     if not results:
         return "No cars detected."
 
     lines = [f"Cars detected: {len(results)}"]
-
     for i, item in enumerate(results, start=1):
-        # handle both 3-tuple and 4-tuple safely
         if len(item) == 4:
             crop, pred, color, conf = item
         else:
             crop, pred, color = item
             conf = None
 
-        # map pred -> human name if possible
-        if isinstance(pred, int):
-            if class_names and 0 <= pred < len(class_names):
-                name = class_names[pred]
-            else:
-                name = f"Class {pred}"
+        if isinstance(pred, int) and class_names and 0 <= pred < len(class_names):
+            name = class_names[pred]
         else:
             name = str(pred)
 
-        # Format with confidence if available
         if conf is not None:
             lines.append(f"Car {i}: {color} {name} ({conf*100:.1f}% confident)")
         else:
@@ -154,28 +83,13 @@ def gradio_interface(image, *args, **kwargs):
 
     return "\n".join(lines)
 
-# Launch Gradio Interface
 iface = gr.Interface(
     fn=gradio_interface,
     inputs=gr.Image(type="pil", label="Upload an Image"),
     outputs=gr.Textbox(label="Detection & Classification Results"),
     title="Car Detector + Classifier (YOLO)",
-    description="Upload a car image and get its color, model, and confidence score.",
+    description="Upload a car image and get its color, model, and confidence score."
 )
-iface.launch(share=True)
-
-# Test the gradio_interface function with the venza.jpg image
-image_path = "/content/venza.jpg"
-try:
-    # Open the image file
-    image = Image.open(image_path)
-    # Call the gradio_interface function, passing class_names
-    test_output = gradio_interface(image, class_names)
-    # Print the output
-    print(test_output)
-except FileNotFoundError:
-    print(f"Error: Image file not found at {image_path}")
-except Exception as e:
-    print(f"An error occurred: {e}")
 
-!grep -n "results" YOLO.ipynb
+if __name__ == "__main__":
+    iface.launch()

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py

@@ -7,9 +7,9 @@ Original file is located at
     https://colab.research.google.com/drive/1gTrf304mzjGMheD47oHDhnYTIrEyf4qp
 """
 
-!pip install gradio --quiet
 import gradio as gr
 from PIL import Image
+import torch
 import os
 
 import os
@@ -178,4 +178,4 @@ except FileNotFoundError:
 except Exception as e:
     print(f"An error occurred: {e}")
 
-!grep -n "results" YOLO.ipynb
+!grep -n "results" YOLO.ipynb

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/car_classifier.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df2189a3b9547272dd7a962f5d05e15a0155c57f7b1e6fee41fb4e698d32666e
+size 45188363

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt

@@ -1 +1,11 @@
-trackio<1.0
+torch
+torchvision
+ultralytics
+gradio
+pillow
+numpy
+matplotlib
+opencv-python
+timm
+transformers
+datasets

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/README.md

@@ -0,0 +1,91 @@
+---
+title: Car Classifier Model
+emoji: 🚗
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: "4.0.0"
+app_file: app.py
+pinned: false
+---
+
+# AiModelCarClassifier
+Creating and Running a Car Classifier Model...
+
+## Car Detector(YOLO + Custom Model)
+
+This project uses **YOLOv5** for car detection and a **custom-trained classifier** for car model recognition and color identification.  
+It takes in any image (JPEG/PNG), detects cars, classifies the car make & model, and outputs color and confidence scores.
+
+Example output:
+- **Cars detected: 1**
+- **Car 1: Gray/Silver Dodge Dakota Crew Cab 2010 (98.7% confident)**
+
+---
+
+## Overview
+
+This project combines **object detection** and **image classification** in one simple pipeline:
+
+1. **YOLOv5** detects cars in the image.  
+2. The detected car regions are cropped and passed into a **PyTorch classifier** (`car_classifier.pth`).  
+3. A small color recognition helper determines the car’s dominant color.  
+4. Results are displayed through a simple **Gradio UI** (or any frontend, e.g. HTML + Flask).
+
+---
+
+## Project Structure
+
+│
+├── YOLO.ipynb # Main notebook for YOLO + classification logic
+├── car_classifier.pth # Trained PyTorch model for car model recognition
+├── app.py # Gradio (or Flask) app for running the interface
+├── class_names.json # (Optional) Human-readable class labels
+├── requirements.txt # Python dependencies
+└── README.md # Project description
+
+---
+
+---
+
+## Works steps
+
+1. **Image Upload** → User uploads an image.  
+2. **YOLOv5 Detection** → Detects car bounding boxes.  
+3. **Classification** → Each car crop is classified using `car_classifier.pth`.  
+4. **Color Recognition** → Extracts car color from the cropped region.  
+5. **Output** → Displays model name, color, and confidence percentage.
+
+---
+## Model Details
+
+- **YOLOv5**: Handles object detection (pretrained on COCO dataset).  
+- **Car Classifier (`car_classifier.pth`)**: Fine-tuned model trained on [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars).  
+- **Color Extractor**: Uses average RGB values to estimate color.
+
+---
+## install depencies
+```
+pip install -r requirements.txt
+```
+
+
+Then open the Gradio or local web interface that appears in your console.
+---
+
+## Setup & Run
+
+Clone the repo:
+
+Then open the Gradio or local web interface that appears in your console.
+```bash
+https://github.com/<Your-Username>/AiModelCarClassifier.git
+cd car-detector-classifier
+```
+
+
+
+## run the app
+```
+python app.py
+```

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py

@@ -0,0 +1,181 @@
+# -*- coding: utf-8 -*-
+"""GradioUI.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1gTrf304mzjGMheD47oHDhnYTIrEyf4qp
+"""
+
+!pip install gradio --quiet
+import gradio as gr
+from PIL import Image
+import os
+
+import os
+from google.colab import files
+
+if not os.path.exists('YOLO.ipynb'):
+    print("Please upload YOLO.ipynb (the script exported from your YOLO notebook).")
+    uploaded = files.upload()  # upload YOLO.ipynb
+    print("Uploaded:", list(uploaded.keys()))
+else:
+    print("YOLO.ipynb already present.")
+
+!ls /content
+
+import nbformat
+from nbconvert import PythonExporter
+import runpy
+
+# Convert YOLO.ipynb to a .py script dynamically
+with open("YOLO.ipynb") as f:
+    nb = nbformat.read(f, as_version=4)
+
+# Find the cell that loads car_classifier.pth and modify the path
+# Also, remove any code that tries to open the notebook file as an image
+modified_cells = []
+for cell in nb.cells:
+    if cell.cell_type == 'code':
+        # This is a heuristic: look for lines containing 'car_classifier.pth'
+        if 'car_classifier.pth' in cell.source:
+            cell.source = cell.source.replace("'car_classifier.pth'", "'/content/car_classifier.pth'")
+            cell.source = cell.source.replace('"car_classifier.pth"', '"/content/car_classifier.pth"')
+
+        # Heuristic to remove code that might try to open the notebook as an image
+        if 'Image.open(' in cell.source and 'YOLO.ipynb' in cell.source:
+             cell.source = '# Removed potential image loading of notebook file:\n#' + cell.source
+
+py_exporter = PythonExporter()
+(code, _) = py_exporter.from_notebook_node(nb)
+
+# Save temporarily as script
+with open("yolo_converted.py", "w") as f:
+    f.write(code)
+
+# Now safely import detect_and_classify() from that converted script
+mod = runpy.run_path("yolo_converted.py")
+detect_and_classify = mod.get("detect_and_classify")
+
+if not detect_and_classify:
+    raise RuntimeError("Function detect_and_classify not found in YOLO.ipynb")
+
+print("✅ YOLO function imported successfully")
+
+import torch, os, json
+pth = "/content/car_classifier.pth"
+print("Exists:", os.path.exists(pth))
+ckpt = torch.load(pth, map_location="cpu")
+print("Type:", type(ckpt))
+
+if isinstance(ckpt, dict):
+    keys = list(ckpt.keys())
+    print("Checkpoint keys (first 20):", keys[:20])
+    # If it's a pure state_dict, it will look like parameter names (e.g. 'conv1.weight')
+    # If it's a wrapped checkpoint, it may contain 'model_state_dict' or 'class_names'
+else:
+    print("Checkpoint is not a dict; it's probably a raw model object.")
+
+!pip install -q datasets
+
+from datasets import load_dataset
+ds = load_dataset("tanganke/stanford_cars")
+# HF dataset provides label names in the train feature
+class_names = ds["train"].features["label"].names
+print("Loaded", len(class_names), "class names. Sample:", class_names[:10])
+
+# Save to disk for reuse
+import json
+with open("class_names.json", "w") as f:
+    json.dump(class_names, f, indent=2)
+print("Saved class_names.json")
+
+import json, os
+if os.path.exists("class_names.json"):
+    with open("class_names.json") as f:
+        class_names = json.load(f)
+    print("Loaded class_names from file, len =", len(class_names))
+else:
+    print("class_names.json not found; run the HF cell above.")
+
+import gradio as gr
+import os
+
+# ensure class_names exists in the notebook (from previous cell)
+try:
+    assert class_names is not None and len(class_names) > 0
+    print("Using class_names with", len(class_names), "entries")
+except Exception:
+    class_names = None
+    print("class_names not available; will show numeric labels")
+
+def gradio_interface(image, *args, **kwargs):
+    if image is None:
+        return "Please upload an image."
+
+    temp_path = "temp_image.png"
+    image.save(temp_path)
+
+    try:
+        results = detect_and_classify(temp_path)  # your notebook function
+    except Exception as e:
+        return f"❌ Error running YOLO pipeline: {e}"
+    finally:
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+
+    if not results:
+        return "No cars detected."
+
+    lines = [f"Cars detected: {len(results)}"]
+
+    for i, item in enumerate(results, start=1):
+        # handle both 3-tuple and 4-tuple safely
+        if len(item) == 4:
+            crop, pred, color, conf = item
+        else:
+            crop, pred, color = item
+            conf = None
+
+        # map pred -> human name if possible
+        if isinstance(pred, int):
+            if class_names and 0 <= pred < len(class_names):
+                name = class_names[pred]
+            else:
+                name = f"Class {pred}"
+        else:
+            name = str(pred)
+
+        # Format with confidence if available
+        if conf is not None:
+            lines.append(f"Car {i}: {color} {name} ({conf*100:.1f}% confident)")
+        else:
+            lines.append(f"Car {i}: {color} {name}")
+
+    return "\n".join(lines)
+
+# Launch Gradio Interface
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Textbox(label="Detection & Classification Results"),
+    title="Car Detector + Classifier (YOLO)",
+    description="Upload a car image and get its color, model, and confidence score.",
+)
+iface.launch(share=True)
+
+# Test the gradio_interface function with the venza.jpg image
+image_path = "/content/venza.jpg"
+try:
+    # Open the image file
+    image = Image.open(image_path)
+    # Call the gradio_interface function, passing class_names
+    test_output = gradio_interface(image, class_names)
+    # Print the output
+    print(test_output)
+except FileNotFoundError:
+    print(f"Error: Image file not found at {image_path}")
+except Exception as e:
+    print(f"An error occurred: {e}")
+
+!grep -n "results" YOLO.ipynb

space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt

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+trackio<1.0