Add application file

.gitattributes

@@ -1,31 +1,16 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,26 @@
 ---
-title: Story Make
-emoji: 📈
-colorFrom: blue
-colorTo: green
+title: YOLOv5
+emoji: 🐨
+colorFrom: indigo
+colorTo: gray
 sdk: gradio
-sdk_version: 3.2
 app_file: app.py
 pinned: false
-license: bsd
 ---
+# Configuration
+`title`: _string_  
+Display title for the Space
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

README1.md

@@ -0,0 +1,25 @@
+# reddit-short-stories
+A small unlabelled dataset of 4,308 short stories (4 million words) scraped from https://reddit.com/r/WritingPrompts for your machine learning needs.
+
+Scraped and formatted by Trevor Du
+
+## Dataset description
+* Each line of [reddit_short_stories.txt](https://github.com/tdude92/reddit-short-stories/blob/main/reddit_short_stories.txt) is one full short story.
+* Each short story begins with an "\<sos>" token and ends with an "\<eos>" token (eg. "\<sos> once upon a time, the end \<eos>").
+* Newline characters in a story are replaced with the "\<nl>" token (eg. "\<sos> line 1 \<nl> line 2 \<eos>")
+
+## Data Collection Method
+r/WritingPrompts is a forum on the popular discussion website, https://reddit.com. The tradition is that users start threads that are titled with a *Writing Prompt*. In these threads, other users comment a short story they've written based on the original prompt.
+
+The scraper saved a comment on a post on r/WritingPrompts if the following conditions are satisfied:
+* The post is flaired "Writing Prompt"
+* The post has >=1.0k upvotes.
+* The author of the comment is not a moderator of r/WritingPrompts (to avoid scraping automod posts and mod announcements).
+* The comment has >=200 upvotes.
+* The comment has >=200 words.
+* <20 comments have already been scraped from the comment's parent post.
+
+Note: Only a portion of r/WritingPrompts was scraped, not the entire thing.
+
+Hoping to scrape more of r/WritingPrompts and other subreddits in the future.
+

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import torch
+from PIL import Image
+from  story import story_model
+
+# Images
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')
+
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
+
+
+def yolo(im, size=640):
+    g = (size / max(im.size))  # gain
+    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
+
+    results = model(im)  # inference
+
+    r= results.pred[0].numpy().T
+    #probs = results.pred[0].numpy().T[-1]
+    res = [(results.names[x].lower(),p) for x,p in zip(r[-1].astype(int),r[-2].numpy())]
+    f_res  = story_model(res)
+    #results.render()  # updates results.imgs with boxes and labels
+    #return Image.fromarray(results.imgs[0])
+    return ','.join(f_res)
+
+
+inputs = gr.inputs.Image(type='pil', label="Original Image")
+#outputs = gr.outputs.Image(type="pil", label="Output Image")
+outputs = gr.outputs.Textbox(type="text", label="Output Story")
+
+title = "YOLOv5"
+description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use."
+article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
+
+examples = [['zidane.jpg'], ['bus.jpg']]
+iface = gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(cache_examples=True,enable_queue=True)
+iface.launch()

out.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ba44ea6fae2933f07a95c2795bc87542728b25228e6f051dc13fae0b895ef7d
+size 10047404

requirements.txt

@@ -0,0 +1,31 @@
+# pip install -r requirements.txt
+
+# base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python-headless
+Pillow
+PyYAML>=5.3.1
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+
+# logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# plotting ------------------------------------
+seaborn>=0.11.0
+pandas
+
+# export --------------------------------------
+# coremltools>=4.1
+# onnx>=1.9.0
+# scikit-learn==0.19.2  # for coreml quantization
+
+# extras --------------------------------------
+# Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
+# pycocotools>=2.0  # COCO mAP
+# albumentations>=1.0.3
+thop  # FLOPs computation

story.py

@@ -0,0 +1,36 @@
+import re
+import numpy as np
+import pickle
+
+
+def load_sentense_data():
+    min_sent_length = 20
+
+    All_sentenses = []
+    with open('reddit_short_stories.txt','r') as f:
+        stories = f.readlines()
+        
+    for story in stories:
+        L = story.split('<nl> _____________ <nl>')[0]
+
+
+        L=L.strip('<sos>').strip('" <eos>')
+
+        L=re.split('; |, |\.|\*|\n|<nl>',L)
+
+        All_sentenses.extend([x.lower() for x in L if len(x)>min_sent_length])
+    
+    return All_sentenses
+
+
+def story_model(preds,res_len=5):
+    with open("out.bin", "rb") as fp:
+        All_sentenses = pickle.load(fp)
+    #All_sentenses = load_sentense_data()
+    dists = np.zeros(len(All_sentenses))
+    for word,prob in preds:
+        dists += np.array([prob * (word in sent) for sent in All_sentenses])
+
+    f_res = list(zip(dists,All_sentenses)).sort(key=lambda x:x[1])[-res_len:]
+
+    return f_res # list of sentenses