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Remove unnecessary files and dependencies #136

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cf33c9a
chore: Remove unnecessary files and dependencies
Devasy23 Jun 3, 2024
121dafc
chore: Remove unnecessary files and dependencies
Devasy23 Jun 3, 2024
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3,921 changes: 2,608 additions & 1,313 deletions AI_NLP_CHATBOT.ipynb
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110 changes: 77 additions & 33 deletions Blockchain-ML medical health .ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,7 @@
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
"from xgboost import XGBClassifier\n",
"\n",
"os.getcwd()"
]
},
Expand All @@ -61,7 +62,7 @@
"metadata": {},
"outputs": [],
"source": [
"os.chdir('/Users/tahirabbas/Desktop/imp')"
"os.chdir(\"/Users/tahirabbas/Desktop/imp\")"
]
},
{
Expand Down Expand Up @@ -102,70 +103,98 @@
}
],
"source": [
"import time \n",
"import time\n",
"\n",
"\n",
"class Blockchain:\n",
" def __init__(self):\n",
" self.chain = []\n",
" self.current_reports = []\n",
" self.create_block(proof=1, previous_hash='0')\n",
" self.create_block(proof=1, previous_hash=\"0\")\n",
" self.reports = {}\n",
"\n",
" def create_block(self, proof, previous_hash):\n",
" block = {\n",
" 'index': len(self.chain) + 1,\n",
" 'timestamp': time.time(), \n",
" 'proof': proof,\n",
" 'previous_hash': previous_hash,\n",
" 'reports': self.current_reports\n",
" \"index\": len(self.chain) + 1,\n",
" \"timestamp\": time.time(),\n",
" \"proof\": proof,\n",
" \"previous_hash\": previous_hash,\n",
" \"reports\": self.current_reports,\n",
" }\n",
" self.chain.append(block)\n",
" self.current_reports = []\n",
" return block\n",
"\n",
"\n",
" def add_report(self, stress_value, diagnosis, key):\n",
" self.reports.setdefault(key, []).append({\n",
" 'stress_value': stress_value,\n",
" 'diagnosis': diagnosis\n",
" })\n",
" self.current_reports.append({\n",
" 'stress_value': stress_value,\n",
" 'diagnosis': diagnosis\n",
" })\n",
" self.reports.setdefault(key, []).append(\n",
" {\"stress_value\": stress_value, \"diagnosis\": diagnosis}\n",
" )\n",
" self.current_reports.append(\n",
" {\"stress_value\": stress_value, \"diagnosis\": diagnosis}\n",
" )\n",
"\n",
" def get_last_block(self):\n",
" return self.chain[-1]\n",
"\n",
" def get_reports_by_key(self, key):\n",
" return self.reports.get(key, [])\n",
"\n",
"\n",
"def load_and_prepare_data():\n",
" df = pd.read_csv('heart.csv')\n",
" df = pd.read_csv(\"heart.csv\")\n",
" label_encoder = LabelEncoder()\n",
" df['Sex'] = label_encoder.fit_transform(df['Sex'])\n",
" df['Blood Pressure'] = df['Blood Pressure'].str.replace('/', '').astype(float)\n",
" df.drop(['Patient ID', 'Diabetes', 'Family History', \"Smoking\", 'Alcohol Consumption', 'Diet', 'Previous Heart Problems', 'Medication Use', 'Sedentary Hours Per Day', 'Income', 'BMI', 'Triglycerides', 'Country', 'Continent', 'Hemisphere', 'Heart Attack Risk', 'Obesity'], axis=1, inplace=True)\n",
" y = df['Stress Level']\n",
" df[\"Sex\"] = label_encoder.fit_transform(df[\"Sex\"])\n",
" df[\"Blood Pressure\"] = df[\"Blood Pressure\"].str.replace(\"/\", \"\").astype(float)\n",
" df.drop(\n",
" [\n",
" \"Patient ID\",\n",
" \"Diabetes\",\n",
" \"Family History\",\n",
" \"Smoking\",\n",
" \"Alcohol Consumption\",\n",
" \"Diet\",\n",
" \"Previous Heart Problems\",\n",
" \"Medication Use\",\n",
" \"Sedentary Hours Per Day\",\n",
" \"Income\",\n",
" \"BMI\",\n",
" \"Triglycerides\",\n",
" \"Country\",\n",
" \"Continent\",\n",
" \"Hemisphere\",\n",
" \"Heart Attack Risk\",\n",
" \"Obesity\",\n",
" ],\n",
" axis=1,\n",
" inplace=True,\n",
" )\n",
" y = df[\"Stress Level\"]\n",
" X = df.drop([\"Stress Level\"], axis=1)\n",
" return X, y\n",
"\n",
"\n",
"def train_model(X, y):\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42\n",
" )\n",
" scaler = StandardScaler()\n",
" X_train_scaled = scaler.fit_transform(X_train)\n",
" X_test_scaled = scaler.transform(X_test)\n",
" rf_clf = RandomForestClassifier(random_state=42)\n",
" xgb_clf = XGBClassifier(random_state=42)\n",
" voting_clf = VotingClassifier(estimators=[('rf', rf_clf), ('xgb', xgb_clf)], voting='soft')\n",
" voting_clf = VotingClassifier(\n",
" estimators=[(\"rf\", rf_clf), (\"xgb\", xgb_clf)], voting=\"soft\"\n",
" )\n",
" voting_clf.fit(X_train_scaled, y_train)\n",
" return voting_clf, scaler\n",
"\n",
"\n",
"def predict_stress_level(model, scaler, user_input):\n",
" user_input_scaled = scaler.transform([user_input])\n",
" predicted_stress_level = model.predict(user_input_scaled)[0]\n",
" return predicted_stress_level\n",
"\n",
"\n",
"def get_diagnosis(stress_level):\n",
" if stress_level <= 3:\n",
" return \"Stress level is in control.\"\n",
Expand All @@ -174,21 +203,34 @@
" else:\n",
" return \"Consult a doctor, your stress level is very high.\"\n",
"\n",
"\n",
"def get_user_input():\n",
" age = float(input(\"Enter Age: \"))\n",
" sex = input(\"Enter Sex (Male/Female): \")\n",
" sex = 1 if sex.lower() == 'male' else 0\n",
" sex = 1 if sex.lower() == \"male\" else 0\n",
" cholesterol = float(input(\"Enter Cholesterol level: \"))\n",
" blood_pressure = float(input(\"Enter Blood Pressure: \"))\n",
" heart_rate = float(input(\"Enter Heart Rate: \"))\n",
" exercise_hours_per_week = float(input(\"Enter Exercise Hours Per Week: \"))\n",
" physical_activity_days_per_week = float(input(\"Enter Physical Activity Days Per Week: \"))\n",
" physical_activity_days_per_week = float(\n",
" input(\"Enter Physical Activity Days Per Week: \")\n",
" )\n",
" sleep_hours_per_day = float(input(\"Enter Sleep Hours Per Day: \"))\n",
" \n",
" user_input = [age, sex, cholesterol, blood_pressure, heart_rate, exercise_hours_per_week, physical_activity_days_per_week, sleep_hours_per_day]\n",
"\n",
" user_input = [\n",
" age,\n",
" sex,\n",
" cholesterol,\n",
" blood_pressure,\n",
" heart_rate,\n",
" exercise_hours_per_week,\n",
" physical_activity_days_per_week,\n",
" sleep_hours_per_day,\n",
" ]\n",
" return user_input\n",
"\n",
"X, y = load_and_prepare_data() \n",
"\n",
"X, y = load_and_prepare_data()\n",
"model, scaler = train_model(X, y)\n",
"user_input = get_user_input()\n",
"predicted_stress_level = predict_stress_level(model, scaler, user_input)\n",
Expand All @@ -197,8 +239,7 @@
"blockchain = Blockchain()\n",
"key = input(\"Enter a key to save the report: \")\n",
"blockchain.add_report(predicted_stress_level, diagnosis, key)\n",
"print(\"Medical report created and saved successfully!\")\n",
"\n"
"print(\"Medical report created and saved successfully!\")"
]
},
{
Expand Down Expand Up @@ -240,13 +281,16 @@
" else:\n",
" return reports\n",
"\n",
"\n",
"key_to_retrieve = input(\"Enter your key to retrieve the reports: \")\n",
"reports = retrieve_reports(blockchain, key_to_retrieve)\n",
"\n",
"if isinstance(reports, list):\n",
" print(\"All medical reports associated with the key:\")\n",
" for i, report in enumerate(reports, start=1):\n",
" print(f\"Report {i}: Stress Value: {report['stress_value']}, Diagnosis: {report['diagnosis']}\")\n",
" print(\n",
" f\"Report {i}: Stress Value: {report['stress_value']}, Diagnosis: {report['diagnosis']}\"\n",
" )\n",
"else:\n",
" print(reports)"
]
Expand All @@ -269,7 +313,7 @@
}
],
"source": [
"df = pd.read_csv('heart.csv')\n",
"df = pd.read_csv(\"heart.csv\")\n",
"df.shape"
]
},
Expand Down
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