v1.0 done

app.py

@@ -1,12 +1,42 @@
 import gradio as gr
 import pandas as pd
 import random
+from keras.models import load_model
+import tensorflow as tf
+import numpy as np
 
 data = pd.read_pickle("merged_all_table.pkl", compression='bz2')
 
 home_team_id = sorted(data["home_team_long_name"].unique())
 away_team_id = sorted(data["away_team_long_name"].unique())
 
+nn_model = load_model('models/nn_model.h5')
+
+
+def main_process(model, Home_team, Away_team):
+
+    home_temp = data[data["home_team_long_name"] == Home_team]
+    home_temp = home_temp[["home_team_overall_score", "home_total_goal", "home_players_avg_overall_rating", "home_players_avg_overall_score", "home_players_avg_ideal_body_rate", "home_total_win", "home_total_loose", "home_total_draw", "league_home_total_win", "league_home_total_loose", "league_home_total_draw"]]
+    print("Home Team Data Geathring ✅")
+
+    away_temp = data[data["away_team_long_name"] == Away_team]
+    away_temp = away_temp[["away_team_overall_score", "away_total_goal", "away_players_avg_overall_rating", "away_players_avg_overall_score", "away_players_avg_ideal_body_rate", "away_total_win", "away_total_loose", "away_total_draw", "league_away_total_win", "league_away_total_loose", "league_away_total_draw"]]
+    print("Away Team Data Geathring ✅")
+
+    table = pd.concat([home_temp.mean(), away_temp.mean()], axis=0)
+    table = table[["home_team_overall_score", "away_team_overall_score", "home_total_goal", "away_total_goal", "home_players_avg_overall_rating", "home_players_avg_overall_score", "home_players_avg_ideal_body_rate", "away_players_avg_overall_rating", "away_players_avg_overall_score", "away_players_avg_ideal_body_rate", "home_total_win", "home_total_loose", "home_total_draw", "away_total_win", "away_total_loose", "away_total_draw", "league_home_total_win", "league_home_total_loose", "league_home_total_draw", "league_away_total_win", "league_away_total_loose", "league_away_total_draw"]]
+    print("Table Concatination ✅")
+
+    X = table.to_frame().T
+
+    pred = model.predict(X)
+    predicted_labels = np.argmax(pred)
+    print("Data Prediction ✅")
+
+    print(predicted_labels)
+
+    return predicted_labels
+
 
 def predict(Home_team, Away_team, Model_name):
 
@@ -19,11 +49,20 @@ def predict(Home_team, Away_team, Model_name):
     if Model_name  == "":
         raise gr.Error("Model is required, Please Select The Model!")
     
-    if Model_name == "Simple Model":
-        # model = km.load_model('models/simple_model.pkl')
-        pass
+    if Model_name == "Simple Nueral Network Model":
+        model = nn_model
+
+    prediction = main_process(model, Home_team, Away_team)
+
+    if prediction == 0:
+        return "🥳 Home Team Win 🎉"
+
+    if prediction == 1:
+        return "🥳 Away Team Win 🎉"
+
+    if prediction == 2:
+        return "😑 Match Draw 😑"
 
-    return "Model is in under construction 🛠️🚜"
 
 # markup table for markdown
 # # Members:
@@ -72,7 +111,7 @@ with gr.Blocks() as demo:
         
             dd_model = gr.Dropdown(
                 label="Model ( Feature Under Construction 🚧 )",
-                choices=["Simple Model"],
+                choices=["Simple Nueral Network Model"],
                 info="Select Your Model:",
                 multiselect=False,
             )

models/{simple_model.pkl → nn_model.h5}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:646659c40f96f43be2e15e1e472f6c8fe4198e4d8c35202becef888c96d50389
-size 2204975
+oid sha256:77acbf1549229bfb38a4e61aa313a3633122cc1d5087978277ef0a3e40607e46
+size 2302560

requirements.txt

@@ -1,11 +1,14 @@
+absl-py==1.4.0
 aiofiles==23.1.0
 aiohttp==3.8.5
 aiosignal==1.3.1
 altair==5.0.1
 annotated-types==0.5.0
 anyio==3.7.1
+astunparse==1.6.3
 async-timeout==4.0.2
 attrs==23.1.0
+cachetools==5.3.1
 certifi==2023.7.22
 charset-normalizer==3.2.0
 click==8.1.6
@@ -15,33 +18,49 @@ exceptiongroup==1.1.2
 fastapi==0.101.0
 ffmpy==0.3.1
 filelock==3.12.2
+flatbuffers==23.5.26
 fonttools==4.42.0
 frozenlist==1.4.0
 fsspec==2023.6.0
+gast==0.4.0
+google-auth==2.22.0
+google-auth-oauthlib==1.0.0
+google-pasta==0.2.0
 gradio==3.39.0
 gradio_client==0.3.0
+grpcio==1.56.2
 h11==0.14.0
+h5py==3.9.0
 httpcore==0.17.3
 httpx==0.24.1
 huggingface-hub==0.16.4
 idna==3.4
+importlib-metadata==6.8.0
 importlib-resources==6.0.0
 Jinja2==3.1.2
 jsonschema==4.18.6
 jsonschema-specifications==2023.7.1
+keras==2.13.1
 kiwisolver==1.4.4
+libclang==16.0.6
 linkify-it-py==2.0.2
+Markdown==3.4.4
 markdown-it-py==2.2.0
 MarkupSafe==2.1.3
 matplotlib==3.7.2
 mdit-py-plugins==0.3.3
 mdurl==0.1.2
 multidict==6.0.4
-numpy==1.25.2
+numpy==1.24.3
+oauthlib==3.2.2
+opt-einsum==3.3.0
 orjson==3.9.3
 packaging==23.1
 pandas==2.0.3
 Pillow==10.0.0
+protobuf==4.24.0
+pyasn1==0.5.0
+pyasn1-modules==0.3.0
 pydantic==2.1.1
 pydantic_core==2.4.0
 pydub==0.25.1
@@ -52,18 +71,28 @@ pytz==2023.3
 PyYAML==6.0.1
 referencing==0.30.2
 requests==2.31.0
+requests-oauthlib==1.3.1
 rpds-py==0.9.2
+rsa==4.9
 semantic-version==2.10.0
 six==1.16.0
 sniffio==1.3.0
 starlette==0.27.0
+tensorflow==2.13.0
+tensorboard==2.13.0
+tensorboard-data-server==0.7.1
+tensorflow-estimator==2.13.0
+tensorflow-macos==2.13.0
+termcolor==2.3.0
 toolz==0.12.0
 tqdm==4.65.0
-typing_extensions==4.7.1
+typing_extensions==4.5.0
 tzdata==2023.3
 uc-micro-py==1.0.2
-urllib3==2.0.4
+urllib3==1.26.16
 uvicorn==0.23.2
 websockets==11.0.3
+Werkzeug==2.3.6
+wrapt==1.15.0
 yarl==1.9.2
 zipp==3.16.2