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You can disable this status message by setting the WalkthroughThis update restructures the backend and frontend of the Perspective project. The backend is refactored to use a modular FastAPI architecture with LangGraph-based processing, Pinecone vector storage, and Groq-powered LLM nodes. Multiple new backend modules and utilities are introduced, while legacy files, routes, and services are removed. The frontend is updated to dynamically render analysis results, refactor loading logic, and streamline the UI with tabbed navigation and session-based data handling. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Frontend
participant BackendAPI
participant LangGraph
participant Pinecone
participant GroqLLM
participant SerpAPI
User->>Frontend: Submit article URL
Frontend->>BackendAPI: POST /api/analyze with URL
BackendAPI->>LangGraph: Start analysis workflow
LangGraph->>GroqLLM: Extract claims from article
GroqLLM-->>LangGraph: Return claims
LangGraph->>SerpAPI: Search web for each claim
SerpAPI-->>LangGraph: Return search results
LangGraph->>GroqLLM: Verify claims with evidence
GroqLLM-->>LangGraph: Return verification verdicts
LangGraph->>GroqLLM: Generate counter-perspective
GroqLLM-->>LangGraph: Return perspective and reasoning
LangGraph->>GroqLLM: Score perspective
GroqLLM-->>LangGraph: Return score
LangGraph->>Pinecone: Store facts and perspective vectors
Pinecone-->>LangGraph: Confirm storage
LangGraph-->>BackendAPI: Return analysis result
BackendAPI-->>Frontend: Return analysis result
Frontend->>User: Display results in tabs
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Actionable comments posted: 26
🔭 Outside diff range comments (1)
backend/app/modules/langgraph_nodes/fact_check.py (1)
21-27: Improve exception handling and logging.The exception handling could be more specific and the logging should use proper logging levels.
except Exception as e: - print(f"some error occured in fact_checking:{e}") + logger.exception(f"Unexpected error in fact checking: {str(e)}") return { "status": "error", "error_from": "fact_checking", - "message": f"{e}", + "message": f"Fact checking failed: {str(e)}", }
🧹 Nitpick comments (13)
backend/start.sh (1)
4-5: Consider checking if uv is already installed before installing.While the current approach works, you could optimize by checking if
uvis already available to avoid unnecessary installation attempts.-# Install uv if not present -pip install uv +# Install uv if not present +if ! command -v uv &> /dev/null; then + pip install uv +fibackend/app/utils/prompt_templates.py (1)
3-32: Consider using a more maintainable prompt format.The multiline string with manual line breaks could be improved for better readability and maintenance. Consider using a cleaner format without the line continuation character.
-generation_prompt = ChatPromptTemplate.from_template(""" -You are an AI assistant that generates a well-reasoned ' -'counter-perspective to a given article. +generation_prompt = ChatPromptTemplate.from_template(""" +You are an AI assistant that generates a well-reasoned counter-perspective to a given article.backend/Dockerfile (2)
5-6: Consider pinning OS package versions for reproducibility.Installing OS packages without version pinning can lead to inconsistent builds across different times.
-RUN apt-get update && apt-get install -y curl build-essential +RUN apt-get update && apt-get install -y \ + curl=7.* \ + build-essential=12.* \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/*
17-18: Optimize Docker layer caching by copying dependency files first.Currently, the entire project is copied before installing dependencies, which means dependency installation will re-run even for code-only changes.
-# Copy project code -COPY . /app +# Copy dependency files first for better caching +COPY pyproject.toml uv.lock ./ + +# Install dependencies +RUN uv sync --locked --no-cache + +# Copy rest of the code +COPY . /appThen move the dependency installation after copying dependency files and before copying the full codebase.
backend/app/db/vector_store.py (1)
16-19: Consider making constants configurable.The hardcoded values work for the current setup, but consider making them configurable via environment variables for different environments.
INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "perspective") DIMENSIONS = int(os.getenv("PINECONE_DIMENSIONS", "384")) METRIC = os.getenv("PINECONE_METRIC", "cosine")backend/app/modules/vector_store/embed.py (1)
4-4: Consider lazy loading the embedder model.Loading the model at module import time may cause unnecessary delays and memory usage if the module is imported but not used.
_embedder = None def get_embedder(): global _embedder if _embedder is None: _embedder = SentenceTransformer("all-MiniLM-L6-v2") return _embedderThen update line 21 to use
get_embedder().encode(texts).tolist().backend/README.md (2)
59-61: Add language specification to code block.The static analysis tool correctly identifies missing language specification for better syntax highlighting.
-``` +```bash http://localhost:8000/api/--- `76-87`: **Add language specification and fix path inconsistency.** The code block needs a language specification, and there's a path inconsistency between the README and the actual setup. ```diff -``` +```bash new-backend/Also, line 28 mentions
cd new-backendbut the structure shows the current directory. Consider clarifying the correct path or updating the clone instruction.backend/app/utils/fact_check_utils.py (1)
40-40: Consider making the rate limit delay configurable.The hardcoded 5-second delay might be too aggressive for development or too lenient for production.
import os # At the top of the file SEARCH_DELAY = float(os.getenv("FACT_CHECK_SEARCH_DELAY", "5.0")) # Then use it: time.sleep(SEARCH_DELAY) # ⏱️ Configurable delay to avoid rate limits.github/workflows/deploy-backend-to-hf.yml (1)
17-17: Consider shallow clone for deployment efficiency.Full git history (
fetch-depth: 0) may be unnecessary for deployment. Consider using a shallow clone to improve performance.- fetch-depth: 0 + fetch-depth: 1backend/app/modules/langgraph_nodes/judge.py (1)
31-36: Simplify response handling logic.The response handling is overly complex for a simple ChatGroq response. ChatGroq responses typically have a consistent
.contentattribute.- if isinstance(response, list) and response: - raw = response[0].content.strip() - elif hasattr(response, "content"): - raw = response.content.strip() - else: - raw = str(response).strip() + raw = response.content.strip()frontend/app/analyze/results/page.tsx (2)
68-70: Simplify badge variant logic for better readability.The nested ternary operator can be simplified.
Consider using an object map:
+const sentimentVariants = { + positive: 'secondary', + negative: 'destructive', + neutral: 'outline' +} as const + -<Badge variant={sentiment === 'positive' ? 'secondary' : sentiment === 'negative' ? 'destructive' : 'outline'} className="capitalize"> +<Badge variant={sentimentVariants[sentiment] || 'outline'} className="capitalize">
48-48: Document that this is a placeholder implementation.The hardcoded response suggests this is a mock implementation.
Consider adding a TODO comment or making the placeholder nature more explicit:
setTimeout(() => { + // TODO: Replace with actual AI backend integration setMessages([...newMessages, { role: "system", content: "Based on the article... let me know if you want more details." }]) }, 1000)
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⛔ Files ignored due to path filters (3)
backend/uv.lockis excluded by!**/*.lockfrontend/package-lock.jsonis excluded by!**/package-lock.jsonnew-backend/uv.lockis excluded by!**/*.lock
📒 Files selected for processing (46)
.github/workflows/deploy-backend-to-hf.yml(1 hunks).gitignore(1 hunks)backend/.dockerignore(1 hunks)backend/Dockerfile(1 hunks)backend/README.md(1 hunks)backend/app/db/vector_store.py(1 hunks)backend/app/main.py(0 hunks)backend/app/modules/facts_check/llm_processing.py(1 hunks)backend/app/modules/facts_check/web_search.py(1 hunks)backend/app/modules/langgraph_builder.py(5 hunks)backend/app/modules/langgraph_nodes/fact_check.py(2 hunks)backend/app/modules/langgraph_nodes/generate_perspective.py(1 hunks)backend/app/modules/langgraph_nodes/judge.py(1 hunks)backend/app/modules/langgraph_nodes/sentiment.py(1 hunks)backend/app/modules/langgraph_nodes/store_and_send.py(1 hunks)backend/app/modules/scraper/cleaner.py(1 hunks)backend/app/modules/vector_store/chunk_rag_data.py(1 hunks)backend/app/modules/vector_store/embed.py(1 hunks)backend/app/prompts/opposite_perspective.py(0 hunks)backend/app/prompts/related_topics.py(0 hunks)backend/app/routes.py(0 hunks)backend/app/scrapers/article_scraper.py(0 hunks)backend/app/scrapers/clean_data.py(0 hunks)backend/app/services/ai_service.py(0 hunks)backend/app/services/analysis_service.py(0 hunks)backend/app/services/counter_service.py(0 hunks)backend/app/services/related_topics.py(0 hunks)backend/app/services/summarization_service.py(0 hunks)backend/app/test_perspective.py(0 hunks)backend/app/utils/fact_check_utils.py(1 hunks)backend/app/utils/generate_chunk_id.py(1 hunks)backend/app/utils/prompt_templates.py(1 hunks)backend/app/utils/store_vectors.py(1 hunks)backend/main.py(1 hunks)backend/pyproject.toml(1 hunks)backend/requirements.txt(0 hunks)backend/start.sh(1 hunks)frontend/app/analyze/loading/page.tsx(2 hunks)frontend/app/analyze/results/page.tsx(3 hunks)frontend/components/bias-meter.tsx(3 hunks)frontend/package.json(2 hunks)new-backend/README.md(0 hunks)new-backend/app/modules/langgraph_nodes/generate_perspective.py(0 hunks)new-backend/app/modules/langgraph_nodes/judge.py(0 hunks)new-backend/app/modules/langgraph_nodes/store_and_send.py(0 hunks)new-backend/main.py(0 hunks)
💤 Files with no reviewable changes (18)
- new-backend/main.py
- backend/requirements.txt
- new-backend/app/modules/langgraph_nodes/generate_perspective.py
- new-backend/app/modules/langgraph_nodes/store_and_send.py
- backend/app/services/related_topics.py
- backend/app/scrapers/article_scraper.py
- backend/app/main.py
- new-backend/README.md
- backend/app/services/analysis_service.py
- new-backend/app/modules/langgraph_nodes/judge.py
- backend/app/services/summarization_service.py
- backend/app/services/counter_service.py
- backend/app/scrapers/clean_data.py
- backend/app/prompts/related_topics.py
- backend/app/test_perspective.py
- backend/app/prompts/opposite_perspective.py
- backend/app/services/ai_service.py
- backend/app/routes.py
🧰 Additional context used
🧬 Code Graph Analysis (4)
backend/app/modules/langgraph_nodes/fact_check.py (1)
backend/app/utils/fact_check_utils.py (1)
run_fact_check_pipeline(10-47)
backend/app/utils/fact_check_utils.py (2)
backend/app/modules/facts_check/web_search.py (1)
search_with_serpapi(5-28)backend/app/modules/facts_check/llm_processing.py (2)
run_claim_extractor_sdk(12-57)run_fact_verifier_sdk(60-132)
backend/app/modules/vector_store/chunk_rag_data.py (1)
backend/app/utils/generate_chunk_id.py (1)
generate_id(4-8)
backend/app/modules/langgraph_builder.py (2)
backend/app/modules/langgraph_nodes/sentiment.py (1)
run_sentiment_sdk(10-53)backend/app/modules/langgraph_nodes/error_handler.py (1)
error_handler(3-11)
🪛 Ruff (0.11.9)
backend/app/modules/langgraph_nodes/store_and_send.py
13-13: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
15-15: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
21-21: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
backend/app/utils/store_vectors.py
32-32: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
backend/app/db/vector_store.py
14-14: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
40-41: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
🪛 markdownlint-cli2 (0.17.2)
backend/README.md
59-59: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
76-76: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🪛 actionlint (1.7.7)
.github/workflows/deploy-backend-to-hf.yml
30-30: shellcheck reported issue in this script: SC2086:info:2:31: Double quote to prevent globbing and word splitting
(shellcheck)
🔇 Additional comments (19)
.gitignore (1)
28-30: LGTM! Appropriate security-focused additions.The new gitignore entries properly exclude GitHub Actions event files and secret files from version control, which is essential for security and CI/CD workflows.
frontend/package.json (1)
41-41: Axios version validated: 1.10.0 is current and secureVerified that 1.10.0 is the latest Axios release (June 14, 2025) and there are no known security vulnerabilities as of July 2025. Approving this dependency addition.
• frontend/package.json, line 41:
"axios": "^1.10.0"backend/app/utils/generate_chunk_id.py (1)
4-8: Well-implemented utility function with proper validation.The
generate_idfunction correctly validates input, uses secure SHA-256 hashing, and returns a consistent format. The 15-character hash substring should provide sufficient uniqueness for article chunk identification.backend/app/modules/langgraph_nodes/sentiment.py (1)
39-39: Good addition for consistency.Converting sentiment to lowercase ensures consistent output format for downstream processing.
backend/.dockerignore (1)
1-2: Standard and appropriate Docker exclusions.The .dockerignore properly excludes virtual environments and environment files from Docker builds, following security and efficiency best practices.
backend/app/modules/scraper/cleaner.py (1)
2-10: Excellent approach for ensuring NLTK dependencies are available.The runtime check for required NLTK corpora is well-implemented and follows best practices. This ensures the text cleaning functionality will work reliably across different environments without manual setup.
backend/pyproject.toml (1)
10-25: Dependencies align well with the new AI/ML functionality.The added packages support the described backend refactoring with search capabilities, LangChain integration, NLP processing, vector storage, and embedding functionality. All are well-established libraries appropriate for the use case.
frontend/components/bias-meter.tsx (1)
1-78: Good stylistic improvements for code consistency.The addition of semicolons and formatting adjustments improves code consistency without affecting functionality. These changes align with TypeScript/JavaScript best practices.
backend/app/utils/store_vectors.py (1)
10-28: Well-structured vector storage utility function.The function design is solid with proper input validation, clear documentation, appropriate logging, and good integration with the Pinecone index. The implementation follows best practices for error handling and type safety.
backend/app/modules/langgraph_nodes/fact_check.py (1)
11-11: Integration Verified:run_fact_check_pipelineReturns a Tuple as Expected
Therun_fact_check_pipeline(state)function inbackend/app/utils/fact_check_utils.py
- Accepts a single
statedict- Returns
([], "<error message>")on various failure paths- Returns
(final.get("verifications", []), None)on successNo changes are needed—its signature and return format match the call site.
backend/app/modules/langgraph_nodes/store_and_send.py (1)
10-15: Fix incorrect status check logic.The status check on line 13 is checking the wrong variable - it should check the
resultfromchunk_rag_data, not the originalstate.try: chunks = chunk_rag_data(state) + # Note: chunk_rag_data doesn't return status, so this check may be unnecessary + # or should check the chunks themselves - except KeyError as e: - raise Exception(f"Missing required data field for chunking: {e}") + except KeyError as e: + raise Exception(f"Missing required data field for chunking: {e}") from e except Exception as e: - raise Exception(f"Failed to chunk data: {e}") + raise Exception(f"Failed to chunk data: {e}") from eLikely an incorrect or invalid review comment.
backend/app/modules/vector_store/embed.py (1)
7-30: Excellent implementation with proper validation.The function properly validates input structure, handles edge cases, and returns a consistent format that integrates well with the vector store operations.
backend/app/modules/vector_store/chunk_rag_data.py (1)
54-55: Fix broken f-string formatting.The f-string is incorrectly split across lines, causing a syntax error.
- raise ValueError("Missing required fact field:" - f" {field} in fact index {i}") + raise ValueError(f"Missing required fact field: {field} in fact index {i}")Likely an incorrect or invalid review comment.
frontend/app/analyze/loading/page.tsx (2)
81-93: LGTM: Well-implemented step progression logic.The step interval logic correctly manages progression through analysis steps and handles cleanup properly.
104-107: LGTM: Proper cleanup implementation.The cleanup function correctly clears both intervals to prevent memory leaks.
backend/app/modules/langgraph_nodes/generate_perspective.py (1)
9-11: LGTM: Well-defined Pydantic model.The PerspectiveOutput model is properly structured with clear field descriptions for the LLM's structured output.
backend/app/modules/langgraph_builder.py (3)
11-22: Good addition of type safety with TypedDict.The MyState class provides clear type definitions for the state dictionary used throughout the graph, improving code maintainability and IDE support.
56-56: Also applies to: 63-63, 72-72, 81-81, 95-95
100-100: Correct use of "end" for graph termination.Using
"__end__"is the proper way to indicate the end of the graph execution in LangGraph.
| model="gemma2-9b-it", | ||
| temperature=0.2, | ||
| max_tokens=10, | ||
| max_tokens=3, |
There was a problem hiding this comment.
Reconsider the max_tokens reduction - may cause response truncation.
Setting max_tokens=3 is too restrictive for sentiment analysis. The expected responses ("Positive", "Negative", "Neutral") require 7-8 characters, which could be truncated with only 3 tokens, potentially causing "Pos", "Neg", or "Neu" responses that may break downstream processing.
- max_tokens=3,
+ max_tokens=10,📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| max_tokens=3, | |
| max_tokens=10, |
🤖 Prompt for AI Agents
In backend/app/modules/langgraph_nodes/sentiment.py at line 35, the max_tokens
parameter is set to 3, which is too low and may truncate the sentiment analysis
output. Increase max_tokens to at least 8 to ensure full words like "Positive",
"Negative", or "Neutral" are returned without truncation, preventing issues in
downstream processing.
| except Exception as e: | ||
| logger.error("Failed to store " | ||
| f"vectors in namespace '{namespace}': {e}") | ||
| raise RuntimeError(f"Vector storage failed: {e}") |
There was a problem hiding this comment.
Fix exception chaining to preserve error context.
The static analysis hint is correct - when re-raising exceptions, use from e to preserve the original exception chain for better debugging.
except Exception as e:
logger.error("Failed to store "
f"vectors in namespace '{namespace}': {e}")
- raise RuntimeError(f"Vector storage failed: {e}")
+ raise RuntimeError(f"Vector storage failed: {e}") from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| except Exception as e: | |
| logger.error("Failed to store " | |
| f"vectors in namespace '{namespace}': {e}") | |
| raise RuntimeError(f"Vector storage failed: {e}") | |
| except Exception as e: | |
| logger.error("Failed to store " | |
| f"vectors in namespace '{namespace}': {e}") | |
| raise RuntimeError(f"Vector storage failed: {e}") from e |
🧰 Tools
🪛 Ruff (0.11.9)
32-32: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
🤖 Prompt for AI Agents
In backend/app/utils/store_vectors.py around lines 29 to 32, the exception is
re-raised without preserving the original exception context. Modify the raise
statement to use 'raise RuntimeError(...) from e' to maintain the exception
chaining and improve debugging.
| app.add_middleware( | ||
| CORSMiddleware, | ||
| allow_origins=["*"], | ||
| allow_credentials=True, | ||
| allow_methods=["*"], | ||
| allow_headers=["*"], | ||
| ) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Review CORS configuration for production security.
The current CORS configuration allows all origins ("*"), credentials, methods, and headers, which poses security risks in production environments. Consider restricting these based on your deployment environment.
-app.add_middleware(
- CORSMiddleware,
- allow_origins=["*"],
- allow_credentials=True,
- allow_methods=["*"],
- allow_headers=["*"],
-)
+import os
+
+# Configure CORS based on environment
+allowed_origins = os.getenv("ALLOWED_ORIGINS", "*").split(",")
+app.add_middleware(
+ CORSMiddleware,
+ allow_origins=allowed_origins,
+ allow_credentials=True,
+ allow_methods=["GET", "POST", "PUT", "DELETE"],
+ allow_headers=["*"],
+)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| import os | |
| # Configure CORS based on environment | |
| allowed_origins = os.getenv("ALLOWED_ORIGINS", "*").split(",") | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=allowed_origins, | |
| allow_credentials=True, | |
| allow_methods=["GET", "POST", "PUT", "DELETE"], | |
| allow_headers=["*"], | |
| ) |
🤖 Prompt for AI Agents
In backend/main.py around lines 14 to 20, the CORS middleware is configured to
allow all origins, credentials, methods, and headers, which is insecure for
production. Update the allow_origins list to specify only trusted domains
instead of "*", and review allow_credentials, allow_methods, and allow_headers
to restrict them to necessary values based on your deployment environment to
enhance security.
| def search_with_serpapi(query, max_results=1): | ||
| api_key = os.getenv("SERPAPI_KEY") | ||
| if not api_key: | ||
| raise ValueError("SERPAPI_KEY not set in environment") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add input validation for the query parameter.
The function should validate that the query parameter is not empty or None to prevent unnecessary API calls.
def search_with_serpapi(query, max_results=1):
+ if not query or not query.strip():
+ raise ValueError("Query parameter cannot be empty")
+
api_key = os.getenv("SERPAPI_KEY")
if not api_key:
raise ValueError("SERPAPI_KEY not set in environment")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def search_with_serpapi(query, max_results=1): | |
| api_key = os.getenv("SERPAPI_KEY") | |
| if not api_key: | |
| raise ValueError("SERPAPI_KEY not set in environment") | |
| def search_with_serpapi(query, max_results=1): | |
| if not query or not query.strip(): | |
| raise ValueError("Query parameter cannot be empty") | |
| api_key = os.getenv("SERPAPI_KEY") | |
| if not api_key: | |
| raise ValueError("SERPAPI_KEY not set in environment") | |
| # …rest of the implementation… |
🤖 Prompt for AI Agents
In backend/app/modules/facts_check/web_search.py around lines 5 to 8, the
function search_with_serpapi lacks validation for the query parameter. Add a
check at the start of the function to ensure the query is not None or an empty
string, and raise a ValueError if this validation fails to prevent unnecessary
API calls.
| search = GoogleSearch(params) | ||
| results = search.get_dict() | ||
| organic = results.get("organic_results", []) | ||
|
|
||
| return [ | ||
| { | ||
| "title": r.get("title", ""), | ||
| "snippet": r.get("snippet", ""), | ||
| "link": r.get("link", ""), | ||
| } | ||
| for r in organic | ||
| ] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for SerpAPI failures.
The function should handle potential API errors from SerpAPI to prevent unhandled exceptions.
- search = GoogleSearch(params)
- results = search.get_dict()
- organic = results.get("organic_results", [])
+ try:
+ search = GoogleSearch(params)
+ results = search.get_dict()
+
+ if "error" in results:
+ raise RuntimeError(f"SerpAPI error: {results['error']}")
+
+ organic = results.get("organic_results", [])
+ except Exception as e:
+ raise RuntimeError(f"Search API failed: {str(e)}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| search = GoogleSearch(params) | |
| results = search.get_dict() | |
| organic = results.get("organic_results", []) | |
| return [ | |
| { | |
| "title": r.get("title", ""), | |
| "snippet": r.get("snippet", ""), | |
| "link": r.get("link", ""), | |
| } | |
| for r in organic | |
| ] | |
| try: | |
| search = GoogleSearch(params) | |
| results = search.get_dict() | |
| if "error" in results: | |
| raise RuntimeError(f"SerpAPI error: {results['error']}") | |
| organic = results.get("organic_results", []) | |
| except Exception as e: | |
| raise RuntimeError(f"Search API failed: {str(e)}") | |
| return [ | |
| { | |
| "title": r.get("title", ""), | |
| "snippet": r.get("snippet", ""), | |
| "link": r.get("link", ""), | |
| } | |
| for r in organic | |
| ] |
🤖 Prompt for AI Agents
In backend/app/modules/facts_check/web_search.py around lines 17 to 28, the code
calls SerpAPI without handling potential errors, which can cause unhandled
exceptions if the API fails. Add a try-except block around the API call and
result processing to catch exceptions, log or handle the error appropriately,
and return a safe default value such as an empty list to ensure the function
fails gracefully.
| try: | ||
| parsed = json.loads(content) | ||
| except Exception as parse_err: | ||
| print(f"❌ LLM JSON parse error: {parse_err}") | ||
|
|
||
| results_list.append(parsed) | ||
|
|
There was a problem hiding this comment.
Fix undefined variable error when JSON parsing fails.
If JSON parsing fails, parsed will be undefined, causing a runtime error when appending to results_list.
Apply this fix:
try:
parsed = json.loads(content)
+ results_list.append(parsed)
except Exception as parse_err:
print(f"❌ LLM JSON parse error: {parse_err}")
-
-results_list.append(parsed)
+ # Skip this result or add error entry
+ continue📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: | |
| parsed = json.loads(content) | |
| except Exception as parse_err: | |
| print(f"❌ LLM JSON parse error: {parse_err}") | |
| results_list.append(parsed) | |
| try: | |
| parsed = json.loads(content) | |
| results_list.append(parsed) | |
| except Exception as parse_err: | |
| print(f"❌ LLM JSON parse error: {parse_err}") | |
| # Skip this result or add error entry | |
| continue |
🤖 Prompt for AI Agents
In backend/app/modules/facts_check/llm_processing.py around lines 113 to 119,
the variable 'parsed' is used outside the try-except block without being defined
if json.loads(content) raises an exception. To fix this, define 'parsed' with a
default value (e.g., None or an empty dict) before the try block or assign it in
the except block to ensure it is always defined before appending to
results_list.
| "claim": claim, | ||
| "verifications": results_list, | ||
| "status": "success", | ||
| } |
There was a problem hiding this comment.
Return comprehensive results instead of just the last claim.
The function currently returns only the last processed claim instead of all claims.
The return statement should include all processed data:
return {
- "claim": claim,
+ "claims": [result.get("claim") for result in search_results],
"verifications": results_list,
"status": "success",
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "claim": claim, | |
| "verifications": results_list, | |
| "status": "success", | |
| } | |
| return { | |
| - "claim": claim, | |
| + "claims": [result.get("claim") for result in search_results], | |
| "verifications": results_list, | |
| "status": "success", | |
| } |
🤖 Prompt for AI Agents
In backend/app/modules/facts_check/llm_processing.py around lines 121 to 124,
the function currently returns only the last processed claim's results. Modify
the return statement to include all processed claims and their corresponding
results, ensuring the output contains comprehensive data for every claim
handled, not just the final one.
| * Provides an interactive interface for users to review an article's summary, explore alternative perspectives, verify factual claims, and engage in AI-assisted discussion. Includes responsive navigation, a bias visualization, and a curated list of references. | ||
| */ | ||
| export default function AnalyzePage() { | ||
| const [analysisData, setAnalysisData] = useState<any>(null) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Define proper TypeScript interface instead of using any.
Using any defeats the purpose of TypeScript's type safety.
Define an interface for the analysis data:
interface AnalysisData {
cleaned_text: string
facts: Array<{
original_claim: string
verdict: string
explanation: string
source_link: string
}>
sentiment: 'positive' | 'negative' | 'neutral'
perspective: {
perspective: string
reasoning: string
}
score: number
}
const [analysisData, setAnalysisData] = useState<AnalysisData | null>(null)🤖 Prompt for AI Agents
In frontend/app/analyze/results/page.tsx at line 20, replace the useState type
from any to a properly defined TypeScript interface. Define an interface named
AnalysisData that includes cleaned_text as string, facts as an array of objects
with original_claim, verdict, explanation, and source_link as strings, sentiment
as a union of 'positive', 'negative', or 'neutral', perspective as an object
with perspective and reasoning strings, and score as a number. Then update the
useState declaration to use AnalysisData or null instead of any.
| <Link href={fact.source_link} target="_blank" className="flex items-center text-sm hover:underline"> | ||
| <LinkIcon className="mr-1 h-4 w-4" /> Source | ||
| </Link> |
There was a problem hiding this comment.
Add security attributes to external links.
External links with target="_blank" should include security attributes to prevent potential vulnerabilities.
-<Link href={fact.source_link} target="_blank" className="flex items-center text-sm hover:underline">
+<Link href={fact.source_link} target="_blank" rel="noopener noreferrer" className="flex items-center text-sm hover:underline">📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| <Link href={fact.source_link} target="_blank" className="flex items-center text-sm hover:underline"> | |
| <LinkIcon className="mr-1 h-4 w-4" /> Source | |
| </Link> | |
| <Link | |
| href={fact.source_link} | |
| target="_blank" | |
| rel="noopener noreferrer" | |
| className="flex items-center text-sm hover:underline" | |
| > | |
| <LinkIcon className="mr-1 h-4 w-4" /> Source | |
| </Link> |
🤖 Prompt for AI Agents
In frontend/app/analyze/results/page.tsx around lines 117 to 119, the external
link using target="_blank" lacks security attributes. Add rel="noopener
noreferrer" to the Link component to prevent security vulnerabilities related to
opening new tabs.
| const storedData = sessionStorage.getItem("analysisResult") | ||
| if (storedData) setAnalysisData(JSON.parse(storedData)) | ||
| else console.warn("No analysis result found") |
There was a problem hiding this comment.
Add error handling for sessionStorage operations.
JSON parsing can throw exceptions if the stored data is malformed.
Wrap the sessionStorage operations in a try-catch:
-const storedData = sessionStorage.getItem("analysisResult")
-if (storedData) setAnalysisData(JSON.parse(storedData))
-else console.warn("No analysis result found")
+try {
+ const storedData = sessionStorage.getItem("analysisResult")
+ if (storedData) {
+ setAnalysisData(JSON.parse(storedData))
+ } else {
+ // Handle missing data appropriately
+ setIsLoading(false)
+ }
+} catch (error) {
+ console.error("Failed to parse analysis data:", error)
+ setIsLoading(false)
+}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| const storedData = sessionStorage.getItem("analysisResult") | |
| if (storedData) setAnalysisData(JSON.parse(storedData)) | |
| else console.warn("No analysis result found") | |
| try { | |
| const storedData = sessionStorage.getItem("analysisResult") | |
| if (storedData) { | |
| setAnalysisData(JSON.parse(storedData)) | |
| } else { | |
| // Handle missing data appropriately | |
| setIsLoading(false) | |
| } | |
| } catch (error) { | |
| console.error("Failed to parse analysis data:", error) | |
| setIsLoading(false) | |
| } |
🤖 Prompt for AI Agents
In frontend/app/analyze/results/page.tsx around lines 35 to 37, the code
retrieves and parses data from sessionStorage without error handling, which can
cause the app to crash if the stored JSON is malformed. Wrap the
sessionStorage.getItem and JSON.parse calls in a try-catch block to catch and
handle any exceptions, logging a warning or error message if parsing fails or
data is missing.
resolving merge conflicts
2 participants
Tasks done-
Results:
Summary by CodeRabbit
New Features
Bug Fixes
Chores
Documentation