Update app.py

app.py

@@ -48,7 +48,7 @@ class GitHubAnalysisResponse(BaseModel):
 # GitHub Repository Analyzer
 class RepoProductivityAnalyzer:
     def __init__(self, github_token: str):
-        if not github_token or github_token == "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":
+        if not github_token or github_token == "YOUR_TOKEN_HERE":
             raise ValueError("Please provide a valid GitHub token")
         
         self.token = github_token
@@ -204,38 +204,51 @@ async def predict_productivity(request: ProductivityRequest):
 @app.post("/analyze-github", response_model=GitHubAnalysisResponse)
 async def analyze_github_repo(request: GitHubAnalysisRequest):
     try:
+        # Validate inputs
+        if not request.repo_url or not request.github_token:
+            raise HTTPException(status_code=422, detail="repo_url and github_token are required")
+        
         analyzer = RepoProductivityAnalyzer(request.github_token)
         metrics = analyzer.get_metrics(request.repo_url)
         
         if "error" in metrics:
             raise HTTPException(status_code=400, detail=metrics["error"])
         
+        # Ensure all required metrics exist with defaults
+        commits_per_day = float(metrics.get('commits_per_day', 0))
+        prs_per_week = float(metrics.get('prs_per_week', 0))
+        issues_per_week = float(metrics.get('issues_per_week', 0))
+        
         # Transform to ML features
         ml_features = {
-            'daily_coding_hours': min(metrics['commits_per_day'] * 2, 8),
-            'commits_per_day': max(int(metrics['commits_per_day']), 0),
-            'pull_requests_per_week': max(int(metrics['prs_per_week']), 0),
-            'issues_closed_per_week': max(int(metrics['issues_per_week']), 0),
+            'daily_coding_hours': min(commits_per_day * 2, 8),
+            'commits_per_day': max(int(commits_per_day), 0),
+            'pull_requests_per_week': max(int(prs_per_week), 0),
+            'issues_closed_per_week': max(int(issues_per_week), 0),
             'active_repos': 1,
-            'code_reviews_per_week': max(int(metrics['prs_per_week']), 0)
+            'code_reviews_per_week': max(int(prs_per_week), 0)
         }
         
         prediction = predict_productivity_core(**ml_features)
         
         productivity_indicators = {
-            'high_commit_frequency': metrics['commits_per_day'] > 1,
-            'active_pr_process': metrics['prs_per_week'] > 2,
-            'good_issue_resolution': metrics['issues_per_week'] > 1,
+            'high_commit_frequency': commits_per_day > 1,
+            'active_pr_process': prs_per_week > 2,
+            'good_issue_resolution': issues_per_week > 1,
             'overall_productivity': prediction > 0.7
         }
         
         return GitHubAnalysisResponse(
-            repo_metrics=metrics, ml_features=ml_features,
-            predicted_score=prediction, productivity_indicators=productivity_indicators,
+            repo_metrics=metrics, 
+            ml_features=ml_features,
+            predicted_score=float(prediction), 
+            productivity_indicators=productivity_indicators,
             status="success"
         )
+    except HTTPException:
+        raise
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
 
 # Gradio Interface Functions
 def gradio_predict(daily_coding_hours, commits_per_day, pull_requests_per_week,