Added a new project #218
Added a new project #218
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Added YouTube Comment Classifier project to the README.
WalkthroughThis pull request introduces a new YouTube Comment Classifier project using zero-shot learning with facebook/bart-large-mnli. It adds a Jupyter notebook implementing classification via Gradio UI with YouTube API integration, accompanied by project documentation, licensing, and required dependencies. Changes
Sequence DiagramsequenceDiagram
participant User
participant Gradio as Gradio UI
participant Notebook as Notebook Logic
participant YouTube as YouTube API
participant Model as Zero-Shot Classifier
participant Display as Results Table
User->>Gradio: Enter YouTube URL
Gradio->>Notebook: Trigger Classification
Notebook->>Notebook: Extract Video ID from URL
Notebook->>YouTube: Fetch Top-Level Comments
YouTube-->>Notebook: Return Comments List
Notebook->>Notebook: Initialize Classifier<br/>(facebook/bart-large-mnli)
loop For Each Comment
Notebook->>Model: Classify Against Candidate Labels
Model-->>Notebook: Return Label & Confidence
Notebook->>Display: Append Result<br/>(comment, label, confidence)
Notebook->>Gradio: Update Results Dataframe
Gradio-->>User: Display Live Results
end
Note over Notebook: Up to 20 comments<br/>classified incrementally
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes
Possibly related PRs
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Pre-merge checks and finishing touches❌ Failed checks (1 inconclusive)
✅ Passed checks (2 passed)
✨ Finishing touches🧪 Generate unit tests (beta)
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Actionable comments posted: 2
🧹 Nitpick comments (4)
youtube-zero-shot-classifier/YouTube_Comment_Classifier_with_Zero_Shot_Learning.ipynb (4)
2076-2076: Consider adding torch to the installation command.The
requirements.txtfile includestorch, but it's missing from this pip install command. Whiletorchgets installed as a transitive dependency oftransformers, explicitly listing it improves clarity and matches the requirements file.Apply this diff:
-!pip install gradio transformers google-api-python-client --quiet +!pip install gradio transformers google-api-python-client torch --quiet
2094-2095: Add validation for missing API key.If
YOUTUBE_API_KEYis not set in Colab userdata, the code will fail later with a cryptic error. Adding early validation improves the user experience.Apply this diff:
# Load API key securely from Colab API_KEY = userdata.get('YOUTUBE_API_KEY') +if not API_KEY: + raise ValueError( + "❌ YOUTUBE_API_KEY not found in Colab secrets. " + "Please add it via the key icon in the left sidebar." + )
2112-2133: Consider documenting YouTube API quota limits.The YouTube Data API has daily quota limits (typically 10,000 units/day for free tier, with comment fetches costing 1 unit per request). For production use, consider adding rate limiting or error handling for quota exceeded errors.
This is not a blocking issue for a beginner demo project, but users should be aware of potential quota limitations if they process many videos.
2148-2148: Setmulti_label=Falsefor better performance.Since you only use the top label (
result["labels"][0]), settingmulti_label=Falsewill be more efficient. Themulti_label=Truemode computes probabilities for all label combinations, which is unnecessary here.Apply this diff:
- result = classifier(comment, candidate_labels=LABELS, multi_label=True) + result = classifier(comment, candidate_labels=LABELS, multi_label=False)
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📒 Files selected for processing (5)
README.md(1 hunks)youtube-zero-shot-classifier/LICENSE(1 hunks)youtube-zero-shot-classifier/README.md(1 hunks)youtube-zero-shot-classifier/YouTube_Comment_Classifier_with_Zero_Shot_Learning.ipynb(1 hunks)youtube-zero-shot-classifier/requirements.txt(1 hunks)
🧰 Additional context used
🪛 Ruff (0.14.8)
youtube-zero-shot-classifier/YouTube_Comment_Classifier_with_Zero_Shot_Learning.ipynb
78-78: Do not catch blind exception: Exception
(BLE001)
79-79: Use explicit conversion flag
Replace with conversion flag
(RUF010)
🔇 Additional comments (4)
README.md (1)
81-81: LGTM!The project entry is well-formatted and appropriately placed in the Beginner Projects section. The description clearly communicates the project's purpose.
youtube-zero-shot-classifier/LICENSE (1)
1-21: LGTM!Standard MIT License properly formatted with appropriate copyright attribution.
youtube-zero-shot-classifier/README.md (1)
1-40: LGTM!The documentation is well-structured and comprehensive. The inclusion of deployment badges and clear feature descriptions makes it easy for users to understand and try the project.
youtube-zero-shot-classifier/YouTube_Comment_Classifier_with_Zero_Shot_Learning.ipynb (1)
2161-2178: LGTM!The Gradio UI is well-structured with clear labels, appropriate components, and good UX design. The dynamic dataframe with live updates provides a nice user experience.
| transformers | ||
| gradio | ||
| google-api-python-client | ||
| torch |
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Pin dependency versions for reproducibility.
Without version constraints, the project may break when dependencies release updates with breaking changes. This is particularly important for transformers and torch, which frequently introduce API changes.
Apply this diff to pin to current stable versions:
-transformers
-gradio
-google-api-python-client
-torch
+transformers>=4.30.0,<5.0.0
+gradio>=4.0.0,<5.0.0
+google-api-python-client>=2.100.0,<3.0.0
+torch>=2.0.0,<3.0.0Alternatively, generate a requirements.txt with exact versions using pip freeze after testing:
#!/bin/bash
# Generate pinned requirements after successful testing
pip freeze | grep -E "^(transformers|gradio|google-api-python-client|torch)==" > requirements.txt🤖 Prompt for AI Agents
In youtube-zero-shot-classifier/requirements.txt around lines 1 to 4, the
dependencies are unpinned which risks breakage from upstream changes; update the
file to pin each package to a specific, tested version (e.g., replace each
package name with package==<version>) and either (a) choose current stable
versions you verified work in CI/dev, or (b) run pip freeze in a validated
environment and write the exact versions for transformers, gradio,
google-api-python-client, and torch into requirements.txt so installs are
reproducible.
| " except Exception as e:\n", | ||
| " yield f\"❌ Error: {str(e)}\", []\n", |
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Catch specific exceptions instead of bare Exception.
Catching all exceptions masks errors and makes debugging harder. The YouTube API can raise specific exceptions like HttpError for quota limits or invalid video IDs, and the classifier can raise RuntimeError for model issues.
Apply this diff:
+from googleapiclient.errors import HttpError
+
# ... (in classify_comments_live function)
- except Exception as e:
- yield f"❌ Error: {str(e)}", []
+ except HttpError as e:
+ if e.resp.status == 403:
+ yield "❌ Error: YouTube API quota exceeded. Please try again later.", []
+ elif e.resp.status == 404:
+ yield "❌ Error: Video not found or comments are disabled.", []
+ else:
+ yield f"❌ YouTube API Error: {str(e)}", []
+ except RuntimeError as e:
+ yield f"❌ Model Error: {str(e)}", []
+ except Exception as e:
+ yield f"❌ Unexpected Error: {str(e)}", []Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In
youtube-zero-shot-classifier/YouTube_Comment_Classifier_with_Zero_Shot_Learning.ipynb
around lines 2157-2158, replace the bare "except Exception as e" with handlers
for the specific errors the code can raise: import and catch
googleapiclient.errors.HttpError to handle YouTube API problems (quota, invalid
IDs) and catch RuntimeError (or the specific model exceptions your classifier
raises) for model-related failures, yielding a clear error message and details
for each; keep a final broad except only as a last-resort fallback (log full
details and re-raise or return a safe failure) so you no longer silently swallow
all exceptions.
1 participant
YouTube Comment Classifier using Zero-Shot Learning
Analyze and classify top comments from any YouTube video into custom intent-style labels using 🤗 Hugging Face's facebook/bart-large-mnli. Built with a clean Gradio UI and deployed on both Google Colab and Hugging Face Spaces.
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