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feat: standardize API response structure across backend and update frontend handling #36

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68 changes: 33 additions & 35 deletions backend/app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,11 +13,25 @@
app = Flask(__name__)
CORS(app)

def success_response(data, message="Classification successful", status_code=200):
return jsonify({
"status": "success",
"message": message,
"data": data,
"error": None
}), status_code

def error_response(message, error=None, status_code=400):
return jsonify({
"status": "error",
"message": message,
"data": None,
"error": error
}), status_code

@app.route('/api/hello', methods=['GET'])
def hello():
return jsonify({'message': 'Hello, World!'})
return success_response({'message': 'Hello, World!'})



Expand All @@ -29,7 +43,7 @@ def classify_aurora():
projectDescription = data.get('projectDescription')

if not projectDescription:
return jsonify({'error': 'Project description is required'}), 400
return error_response('Project description is required', status_code=400)



Expand All @@ -45,10 +59,7 @@ def classify_aurora():
print("Aurora API model completed successfully")
except Exception as e:
print(f"Aurora API model failed: {str(e)}")
return jsonify({
"error": str(e),
"message": "Aurora API classification failed"
}), 500
return error_response("Aurora API classification failed", error=str(e), status_code=500)

# Transform predictions to array format
sdg_preds = aurora_result.get("sdg_predictions", {})
Expand All @@ -62,11 +73,11 @@ def classify_aurora():

filtered_predictions = [p for p in preds if p.get("prediction", 0) > 0.4]

return jsonify({
return success_response({
"projectName": aurora_result.get("project_name"),
"projectUrl": aurora_result.get("project_url"),
"predictions": filtered_predictions
}), 200
})


@app.route('/api/classify_st_description', methods=['POST'])
Expand All @@ -77,7 +88,7 @@ def classify_st_description():
projectDescription = data.get('projectDescription')

if not projectDescription:
return jsonify({'error': 'Project description is required'}), 400
return error_response('Project description is required', status_code=400)


# 3. Sentence Transformer Description Model (text-based)
Expand All @@ -92,11 +103,7 @@ def classify_st_description():
print("ST Description model completed successfully")
except Exception as e:
print(f"ST Description model failed: {str(e)}")
st_desc_result = {
"error": str(e),
"message": "Sentence Transformer Description model classification failed"
}
return jsonify(st_desc_result), 500
return error_response("Sentence Transformer Description model classification failed", error=str(e), status_code=500)

# Convert st-description-model predictions to the expected format for logging
# (keeping backward compatibility with existing data/predictions.json structure)
Expand All @@ -105,11 +112,11 @@ def classify_st_description():
for name, score in st_desc_result.get("sdg_predictions", {}).items()
]
filtered_predictions = [p for p in preds if p.get("prediction", 0) > 0.4]
return jsonify({
return success_response({
"projectName": projectName,
"projectUrl": projectUrl,
"predictions": filtered_predictions,
}), 200
})


@app.route('/api/classify_st_url', methods=['POST'])
Expand All @@ -120,7 +127,7 @@ def classify_st_url():
projectDescription = data.get('projectDescription')

if not projectDescription:
return jsonify({'error': 'Project description is required'}), 400
return error_response('Project description is required', status_code=400)



Expand All @@ -133,19 +140,13 @@ def classify_st_url():
print("ST URL model completed successfully")
except ValueError as ve:
print(f"ST URL model invalid URL: {str(ve)}")
return jsonify({'error': str(ve)}), 400
return error_response(str(ve), status_code=400)
except requests.exceptions.HTTPError as he:
print(f"ST URL model HTTP Error: {str(he)}")
return jsonify({
"error": f"Failed to fetch repository data. Please ensure the repository is public and exists. ({str(he)})",
"message": "Sentence Transformer URL model classification failed"
}), 400
return error_response("Sentence Transformer URL model classification failed", error=f"Failed to fetch repository data. Please ensure the repository is public and exists. ({str(he)})", status_code=400)
except Exception as e:
print(f"ST URL model failed: {str(e)}")
return jsonify({
"error": str(e),
"message": "Sentence Transformer URL model classification failed"
}), 500
return error_response("Sentence Transformer URL model classification failed", error=str(e), status_code=500)
else:
st_url_result = {
"message": "No project URL provided, skipping URL-based classification"
Expand All @@ -156,11 +157,11 @@ def classify_st_url():
for name, score in st_url_result.get("sdg_predictions", {}).items()
]
filtered_predictions = [p for p in preds if p.get("prediction", 0) > 0.4]
return jsonify({
return success_response({
"projectName": projectName,
"projectUrl": projectUrl,
"predictions": filtered_predictions,
}), 200
})

@app.route("/api/osdg_api", methods=["POST"])
def osdg_external_api():
Expand All @@ -170,7 +171,7 @@ def osdg_external_api():
projectDescription = data.get('projectDescription')

if not projectDescription:
return jsonify({'error': 'Project description is required'}), 400
return error_response('Project description is required', status_code=400)

# Call the external OSDG API
try:
Expand All @@ -188,16 +189,13 @@ def osdg_external_api():
osdg_result = osdg_response.json()
except requests.exceptions.RequestException as e:
print(f"OSDG API request failed: {str(e)}")
return jsonify({
"error": f"Failed to connect to OSDG API: {str(e)}",
"message": "OSDG API classification failed"
}), 500
return error_response("OSDG API classification failed", error=f"Failed to connect to OSDG API: {str(e)}", status_code=500)

return jsonify({
return success_response({
"projectName": projectName,
"projectUrl": projectUrl,
"predictions": osdg_result
}), 200
})
# @app.post("/api/upload-md")
# def aurora_api():

Expand Down
5 changes: 3 additions & 2 deletions frontend/components/mainScreen.tsx
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Original file line number Diff line number Diff line change
Expand Up @@ -89,9 +89,10 @@ const MainScreen: React.FC<{

// console.log("API Response:", response.data);
setResults(response as ResultsData);
} catch (error) {
} catch (error: any) {
console.error("Error:", error);
setUploadMsg("Text Analyzing Failed. Please try again.");
const backendMessage = error.response?.data?.message;
setUploadMsg(backendMessage || "Text Analyzing Failed. Please try again.");
} finally {
setIsUploading(false);
}
Expand Down
12 changes: 6 additions & 6 deletions frontend/services/api.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -18,31 +18,31 @@ export const sdgApi = {
classifyAurora: async (
data: SDGClassificationRequest,
): Promise<SDGClassificationResponse> => {
const response = await apiClient.post<SDGClassificationResponse>(
const response = await apiClient.post<{ data: SDGClassificationResponse }>(
"api/classify_aurora",
data,
);
return response.data;
return response.data.data;
},

classifySTDescription: async (
data: SDGClassificationRequest,
): Promise<SDGClassificationResponse> => {
const response = await apiClient.post<SDGClassificationResponse>(
const response = await apiClient.post<{ data: SDGClassificationResponse }>(
"api/classify_st_description",
data,
);
return response.data;
return response.data.data;
},

classifySTUrl: async (
data: SDGClassificationRequest,
): Promise<SDGClassificationResponse> => {
const response = await apiClient.post<SDGClassificationResponse>(
const response = await apiClient.post<{ data: SDGClassificationResponse }>(
"api/classify_st_url",
data,
);
return response.data;
return response.data.data;
},
};

Expand Down