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Update app.py
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app.py
CHANGED
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@@ -185,9 +185,10 @@ def generate_report(buf: bytes, name: str, ctx: str, key: str):
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"columns": list(df.columns),
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"user_ctx": ctx or "General business analysis",
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"full_dataframe": df.to_dict('records'),
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"data_types": df.dtypes.to_dict(),
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"missing_values": df.isnull().sum().to_dict(),
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"numeric_summary":
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}
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cols = ", ".join(ctx_dict["columns"][:6])
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@@ -292,6 +293,7 @@ def generate_report(buf: bytes, name: str, ctx: str, key: str):
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}
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# βββ ANIMATION HELPERS βββββββββββββββββββββββββββββββββββββββββββββββββββββ
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def animate_image_fade(img_cv2: np.ndarray, dur: float, out: Path, fps: int = FPS) -> str:
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frames = max(int(dur * fps), fps)
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@@ -504,8 +506,9 @@ def generate_video(buf: bytes, name: str, ctx: str, key: str):
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"columns": list(df.columns),
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"user_ctx": ctx or "General business analysis",
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"full_dataframe": df.to_dict('records'),
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"data_types": df.dtypes.to_dict(),
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"numeric_summary":
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}
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script = llm.invoke(build_story_prompt(ctx_dict)).content
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"columns": list(df.columns),
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"user_ctx": ctx or "General business analysis",
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"full_dataframe": df.to_dict('records'),
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"data_types": {col: str(dtype) for col, dtype in df.dtypes.to_dict().items()},
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"missing_values": {col: int(count) for col, count in df.isnull().sum().to_dict().items()},
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"numeric_summary": {col: {stat: float(val) for stat, val in stats.items()}
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for col, stats in df.describe().to_dict().items()} if len(df.select_dtypes(include=['number']).columns) > 0 else {}
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}
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cols = ", ".join(ctx_dict["columns"][:6])
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}
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# βββ ANIMATION HELPERS βββββββββββββββββββββββββββββββββββββββββββββββββββββ
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def animate_image_fade(img_cv2: np.ndarray, dur: float, out: Path, fps: int = FPS) -> str:
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frames = max(int(dur * fps), fps)
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"columns": list(df.columns),
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"user_ctx": ctx or "General business analysis",
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"full_dataframe": df.to_dict('records'),
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"data_types": {col: str(dtype) for col, dtype in df.dtypes.to_dict().items()},
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"numeric_summary": {col: {stat: float(val) for stat, val in stats.items()}
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for col, stats in df.describe().to_dict().items()} if len(df.select_dtypes(include=['number']).columns) > 0 else {}
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}
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script = llm.invoke(build_story_prompt(ctx_dict)).content
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