Upload 2 files

app.py

@@ -1,39 +1,63 @@
 import gradio as gr
 import pandas as pd
 
-df_original = pd.read_csv('original.csv')
-df_predicted = pd.read_csv('predicted.csv')
+from fastai.tabular.all import *
+from fastai.layers import Module, Embedding, sigmoid_range
 
-def load_examples():
-    st = []
-    for i in range(10):
-        st.append(';'.join(map(str, df_original.iloc[i])))
-    return st
+import torch
+import torch.nn.functional as F
 
-examples = load_examples()
+class DotProductBias(Module):
+    def __init__(self, n_users, n_games, n_factors, y_range=(0.1, 11754.0)):
+        self.user_factors = Embedding(n_users, n_factors)
+        self.user_bias = Embedding(n_users, 1)
+        self.game_factors = Embedding(n_games, n_factors)
+        self.game_bias = Embedding(n_games, 1)
+        self.y_range = y_range
 
-def check_equal(original,predicted):
-    percentage = 1
-    lower = original * (1 - percentage)
-    upper = original * (1 + percentage)
-    return (predicted >= lower and predicted <= upper)
+    def forward(self, x):
+        users = self.user_factors(x[:, 0])
+        games = self.game_factors(x[:, 1])
+        res = (users * games).sum(dim=1, keepdim=True)
+        res += self.user_bias(x[:, 0]) + self.game_bias(x[:, 1])
+        return torch.sigmoid(res) * (self.y_range[1] - self.y_range[0]) + self.y_range[0]
 
 
+df = pd.read_csv('original.csv')
 
-def predict(user_game_time):
-    (user_id, game_name, time_played) = user_game_time.split(';')
+path = Path("./model.pkl")
+learn = load_learner(path, 'model.pkl')
+
+
+examples = list(df['user-id'].unique())[:20]
+
+all_games_title = list(df['game-title'].unique())
+
+
+def predict(user_id):
 
     user_id = int(user_id)
-    time_played_original = float(time_played)
 
-    # Search query
-    time_played_predicted = df_predicted[(df_predicted['user-id'] == user_id) & (df_predicted['game-title'] == game_name)].iloc[0,2]
-    # Check
-    right_predict = check_equal(time_played_original, time_played_predicted)
-    equal = "Equal" if right_predict else  "Not Equal"
+    games_alredy_played = list(df[(df['user-id'] == user_id)]['game-title'].unique())
+
+    games_not_played = set(all_games_title) - set(games_alredy_played)
+    size_games_not_played = len(games_not_played)
+
+    data = {
+            'user-id': [user_id]* size_games_not_played,
+            'game-title': list(games_not_played),
+            'time-played': [0]* size_games_not_played
+        }
 
-    ans = f"Time original: {time_played_original}\n Time predicted: {time_played_predicted}\n They are equal: {equal}"
+    new_df = pd.DataFrame(data)
+    new_dl = learn.dls.test_dl(new_df)
+    predictions = learn.get_preds(dl=new_dl)
+    predicted_time_played = predictions[0].squeeze().tolist()
+    new_df['time-played'] = predicted_time_played
+    recomendations = new_df.sort_values(by='time-played', ascending=False).head(10)['game-title'].tolist()
+    st = [r + '\n' for r in recomendations]
 
+    ans =  ''.join(st)
     return ans
 
 

requirements.txt

@@ -1,65 +1,147 @@
-aiofiles==23.2.1
-altair==5.2.0
-annotated-types==0.6.0
-anyio==3.7.1
-attrs==23.1.0
-certifi==2023.11.17
-charset-normalizer==3.3.2
-click==8.1.7
-colorama==0.4.6
-contourpy==1.2.0
-cycler==0.12.1
-exceptiongroup==1.2.0
-fastapi==0.104.1
-ffmpy==0.3.1
-filelock==3.13.1
-fonttools==4.46.0
-fsspec==2023.12.0
-gradio==4.7.1
-gradio_client==0.7.0
-h11==0.14.0
-httpcore==1.0.2
-httpx==0.25.2
-huggingface-hub==0.19.4
-idna==3.6
-importlib-resources==6.1.1
+apturl==0.5.2
+attrs==21.2.0
+blinker==1.4
+blis==0.7.9
+Brlapi==0.8.3
+catalogue==2.0.8
+certifi==2020.6.20
+chardet==4.0.0
+click==8.0.3
+cmake==3.26.4
+colorama==0.4.4
+command-not-found==0.3
+confection==0.1.0
+contourpy==1.1.0
+cryptography==3.4.8
+cupshelpers==1.0
+cycler==0.11.0
+cymem==2.0.7
+dbus-python==1.2.18
+defer==1.0.6
+distlib==0.3.4
+distro==1.7.0
+distro-info===1.1build1
+docker==5.0.3
+docker-compose==1.29.2
+dockerpty==0.4.1
+docopt==0.6.2
+fastai==2.7.12
+fastcore==1.5.29
+fastdownload==0.0.7
+fastprogress==1.0.3
+filelock==3.6.0
+fonttools==4.41.0
+httplib2==0.20.2
+idna==3.3
+importlib-metadata==4.6.4
+jeepney==0.7.1
 Jinja2==3.1.2
-jsonschema==4.20.0
-jsonschema-specifications==2023.11.2
-kiwisolver==1.4.5
-markdown-it-py==3.0.0
+joblib==1.3.1
+jsonschema==3.2.0
+keyring==23.5.0
+kiwisolver==1.4.4
+langcodes==3.3.0
+language-selector==0.1
+launchpadlib==1.10.16
+lazr.restfulclient==0.14.4
+lazr.uri==1.0.6
+lit==16.0.6
+louis==3.20.0
+macaroonbakery==1.3.1
 MarkupSafe==2.1.3
-matplotlib==3.8.2
-mdurl==0.1.2
-numpy==1.26.2
-orjson==3.9.10
-packaging==23.2
-pandas==2.1.3
-Pillow==10.1.0
-pydantic==2.5.2
-pydantic_core==2.14.5
-pydub==0.25.1
-Pygments==2.17.2
-pyparsing==3.1.1
+matplotlib==3.7.2
+more-itertools==8.10.0
+mpmath==1.3.0
+murmurhash==1.0.9
+netifaces==0.11.0
+networkx==3.1
+numpy==1.26.1
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+oauthlib==3.2.0
+olefile==0.46
+packaging==23.1
+pandas==2.0.3
+pandas-stubs==2.1.1.230928
+pathy==0.10.2
+pexpect==4.8.0
+Pillow==9.0.1
+platformdirs==2.5.1
+preshed==3.0.8
+protobuf==3.12.4
+psutil==5.9.0
+ptyprocess==0.7.0
+pycairo==1.20.1
+pycups==2.0.1
+pydantic==1.10.11
+Pygments==2.11.2
+PyGObject==3.42.1
+pyinotify==0.9.6
+PyJWT==2.3.0
+pymacaroons==0.13.0
+PyNaCl==1.5.0
+pyparsing==2.4.7
+pyRFC3339==1.1
+pyrsistent==0.18.1
+python-apt==2.4.0+ubuntu1
 python-dateutil==2.8.2
-python-multipart==0.0.6
-pytz==2023.3.post1
-PyYAML==6.0.1
-referencing==0.31.1
-requests==2.31.0
-rich==13.7.0
-rpds-py==0.13.2
-semantic-version==2.10.0
-shellingham==1.5.4
+python-debian===0.1.43ubuntu1
+python-dotenv==0.19.2
+python-pam==1.8.4
+python-xapp==2.2.1
+python-xlib==0.29
+pytz==2022.1
+pyxdg==0.27
+PyYAML==5.4.1
+reportlab==3.6.8
+requests==2.25.1
+scikit-learn==1.3.0
+scipy==1.11.1
+screen-resolution-extra==0.0.0
+SecretStorage==3.3.1
+setproctitle==1.2.2
 six==1.16.0
-sniffio==1.3.0
-starlette==0.27.0
-tomlkit==0.12.0
-toolz==0.12.0
-tqdm==4.66.1
+smart-open==6.3.0
+spacy==3.6.0
+spacy-legacy==3.0.12
+spacy-loggers==1.0.4
+srsly==2.4.6
+ssh-import-id==5.11
+sympy==1.12
+systemd-python==234
+texttable==1.6.4
+thinc==8.1.10
+threadpoolctl==3.2.0
+tinycss==0.4
+tinycss2==1.1.1
+torch==2.0.1
+torchvision==0.15.2
+tqdm==4.65.0
+triton==2.0.0
 typer==0.9.0
-typing_extensions==4.8.0
+types-pytz==2023.3.1.1
+typing_extensions==4.7.1
 tzdata==2023.3
-urllib3==2.1.0
-uvicorn==0.24.0.post1
-websockets==11.0.3
+ubuntu-advantage-tools==8001
+ubuntu-drivers-common==0.0.0
+ufw==0.36.1
+unattended-upgrades==0.1
+urllib3==1.26.5
+virtualenv==20.13.0+ds
+wadllib==1.3.6
+wasabi==1.1.2
+webencodings==0.5.1
+websocket-client==1.2.3
+xdg==5
+xkit==0.0.0
+xlrd==1.2.0
+zipp==1.0.0