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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+share/python-wheels/
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+.installed.cfg
+*.egg
+MANIFEST
+
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+
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+
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+htmlcov/
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+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+
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+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
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+
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+
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+.env
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+cython_debug/
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+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
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+# Marimo
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+marimo/_lsp/
+__marimo__/
+DSCS553_CS1_Assignment.pdf

LICENSE

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+MIT License
+
+Copyright (c) 2025 Shreya Boyane
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-title: Scene Mood Classifier
-emoji: 🏃
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 5.44.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: Multimodal (Vision + Audio) Fusion app
----
+# Case-Study-1
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Hi There

fusion-app/app_local.py

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+import gradio as gr
+import time
+import json
+import numpy as np
+from pathlib import Path
+
+lables = [x ["name"] for x in json.load(Path("labels.json").read_text())["labels"]]
+
+def predict_vid(video):
+    t0= time.time()
+    probs = np.ones(len(lables))/len(lables)
+    pred = lables[int(np.argmax(probs))]
+    lat = {"t_total_ms": int((time.time()-t0)*1000), "note": "dummy"}
+    return pred, {k: float(v) for k,v in zip(lables, probs)}, lat
+
+def predict_aud(audio):
+    t0 = time.time()
+    probs = np.ones(len(lables)) / len(lables)
+    pred = lables[int(np.argmax(probs))]
+    lat = {"t_total_ms": int((time.time()-t0)*1000), "note": "dummy"}
+    return pred, {k: float(v) for k,v in zip(lables, probs)}, lat
+
+
+with gr.Blocks(title="Scene Mood Detection") as demo:
+    gr.Markdown("# Scene Mood Classifier\nUpload a short **video** or an **image + audio** pair.")
+    with gr.Tab("Video"):
+        v = gr.Video(sources=["upload"], height=240)
+        btn_v = gr.Button("Analyze")
+        out_v1 = gr.Label(label="Prediction")
+        out_v2 = gr.JSON(label="Probabilities")
+        out_v3 = gr.JSON(label="Latency (ms)")
+        btn_v.click(predict_video, inputs=[v], outputs=[out_v1,out_v2,out_v3])
+    with gr.Tab("Image + Audio"):
+        img = gr.Image(type="pil", height=240)
+        aud = gr.Audio(sources=["upload"], type="filepath")
+        btn_ia = gr.Button("Analyze")
+        out_i1 = gr.Label(label="Prediction")
+        out_i2 = gr.JSON(label="Probabilities")
+        out_i3 = gr.JSON(label="Latency (ms)")
+        btn_ia.click(predict_image_audio, inputs=[img,aud], outputs=[out_i1,out_i2,out_i3])
+
+if __name__ == "__main__":
+    demo.launch()

fusion-app/labels.json

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+{
+  "labels": [
+    {"name": "calm",      "prompt": "a tranquil, peaceful scene",         "def": "low motion, soft colors, quiet audio"},
+    {"name": "energetic", "prompt": "a high-energy lively scene",         "def": "fast motion, bright colors, loud/fast audio"},
+    {"name": "suspense",  "prompt": "a tense, foreboding scene",          "def": "dim colors, slow build, ominous drones"},
+    {"name": "joyful",    "prompt": "a happy, upbeat, celebratory scene", "def": "warm colors, smiles, upbeat music"},
+    {"name": "sad",       "prompt": "a somber, gloomy scene",             "def": "cool/dark tones, slow pace, quiet audio"}
+  ]
+}

fusion-app/requirements.txt

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+gradio
+transformers
+torch
+torchaudio
+torchvision
+pydub
+ffmpeg-python
+numpy
+pytest
+huggingface_hub
+datasets

fusion-app/tests/test_shapes.py

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+import numpy as np
+def test_concat_dim():
+    img, aud = np.random.randn(512), np.random.randn(768)
+    assert (img.size + aud.size) == 1280

fusion-app/tests/test_smoke.py

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+def test_imports():
+    import gradio, numpy  # noqa
+    assert True