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app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+from nltk.stem import WordNetLemmatizer
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.metrics import classification_report
+from sklearn.compose import ColumnTransformer
+from sklearn.impute import SimpleImputer
+from textblob import TextBlob
+import nltk
+nltk.download('punkt')
+nltk.download('wordnet')
+import numpy as np
+
+# Load pre-trained models and preprocessor objects
+model = joblib.load('pet_adoption.pkl')
+preprocessor = joblib.load('preprocessor.pkl')
+
+# Lemmatizer for text preprocessing
+lemmatizer = WordNetLemmatizer()
+
+# Function to preprocess text input
+def preprocess_text(text):
+    text = text.lower()
+    text = text.replace('[^\w\s]','',regex=True)
+    text = text.replace('\s+',' ',regex=True)
+    text = text.replace('\n',' ',regex=True)
+    text = text.replace('\d+',' ',regex=True)
+    stop_words = set(stopwords.words('english'))
+    words = nltk.word_tokenize(text)
+    words = [word for word in words if word not in stop_words]
+ 
+    # Lemmatization
+    words = [lemmatizer.lemmatize(word) for word in words]
+    # Join processed words
+    return ' '.join(words)
+
+
+# Duygu analizi fonksiyonu
+def analyze_sentiment(description):
+    analysis = TextBlob(description)
+    # Sentiment.polarity değeri -1 ile 1 arasında değişir: -1 negatif, 0 nötr, 1 pozitif
+    return analysis.sentiment.polarity
+
+def sentiment_to_adoption_speed(sentiment):
+    if sentiment > 0.2:
+        return 2  # Yüksek olumlu duygu
+    elif sentiment < -0.2:
+        return 0  # Yüksek olumsuz duygu
+    else:
+        return 1  # Nötr veya orta duygu
+
+
+
+# Gradio arayüzü için girdi alanları ve işlemler
+def predict_adoption_speed(age, breed1, breed2, gender, color1, color2, color3,
+                           maturity_size, fur_length, vaccinated, dewormed,
+                           sterilized, health, quantity, fee, state, video_amt,
+                           photo_amt, description):
+    
+    # Convert input into DataFrame format
+    data = pd.DataFrame({
+        'Age': [age],
+        'Breed1': [breed1],
+        'Breed2': [breed2],
+        'Gender': [gender],
+        'Color1': [color1],
+        'Color2': [color2],
+        'Color3': [color3],
+        'MaturitySize': [maturity_size],
+        'FurLength': [fur_length],
+        'Vaccinated': [vaccinated],
+        'Dewormed': [dewormed],
+        'Sterilized': [sterilized],
+        'Health': [health],
+        'Quantity': [quantity],
+        'Fee': [fee],
+        'State': [state],
+        'VideoAmt': [video_amt],
+        'PhotoAmt': [photo_amt],
+        'sentiments': [sentiments]
+    })
+    
+    # Separate numerical and categorical columns
+    num_columns = ['Age', 'Quantity', 'Fee', 'VideoAmt', 'PhotoAmt']
+    cat_columns = ['Breed1', 'Breed2', 'Gender', 'Color1', 'Color2', 'Color3',
+                   'MaturitySize', 'FurLength', 'Vaccinated', 'Dewormed',
+                   'Sterilized', 'Health', 'State']
+    
+    # Preprocess numerical and categorical data
+    processed_data = preprocessor.transform(data)
+    
+    # Convert description text to sentiments
+    cleaned_description = preprocess_text(description)
+    sentiment = analyze_sentiment(cleaned_description)
+    sentiments = sentiment_to_adoption_speed(sentiment)  # Placeholder for sentiment analysis
+    
+    # Make prediction
+    prediction = model.predict(processed_data)
+    
+    # Return prediction result
+    return int(prediction[0])
+
+# Gradio arayüzünü oluşturma
+
+# Define Gradio Interface
+iface = gr.Interface(
+    fn=predict_adoption_speed, 
+    inputs=[
+        gr.Slider(minimum=0, maximum=20, default=1, label="Age"),
+        gr.Number(label="Breed1"),
+        gr.Number(label="Breed2"),
+        gr.Dropdown(choices=[0, 1, 2], label="Gender"),
+        gr.Dropdown(choices=[1, 2, 3, 4, 5, 6, 7], label="Color1"),
+        gr.Dropdown(choices=[0, 1, 2, 3, 4, 5, 6, 7], label="Color2"),
+        gr.Dropdown(choices=[0, 1, 2, 3, 4, 5, 6, 7], label="Color3"),
+        gr.Dropdown(choices=[0, 1, 2, 3], label="MaturitySize"),
+        gr.Dropdown(choices=[1, 2, 3], label="FurLength"),
+        gr.Dropdown(choices=[0, 1], label="Vaccinated"),
+        gr.Dropdown(choices=[0, 1], label="Dewormed"),
+        gr.Dropdown(choices=[0, 1], label="Sterilized"),
+        gr.Dropdown(choices=[0, 1], label="Health"),
+        gr.Number(label="Quantity"),
+        gr.Number(label="Fee"),
+        gr.Dropdown(choices=[41324, 41325, 41326, 41327, 41330, 41332, 41335, 41336, 41342, 41345, 41361, 41367, 41401, 41415, 41452], label="State"),
+        gr.Slider(minimum=0, maximum=10, default=0, label="VideoAmt"),
+        gr.Slider(minimum=0, maximum=10, default=0, label="PhotoAmt"),
+        gr.Textbox(label="Description", placeholder="Enter pet description...")
+    ],
+    outputs=gr.outputs.Textbox(label="Predicted Adoption Speed")
+)
+
+# Launch Gradio Interface
+iface.launch(share=True)
+

disaster_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e24f49b1850c7d28a654c0b21f70213ed405d47520e53601b8fa6e8de258df55
+size 602806

pet_adoption.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1fc0db89c54fa4101b3cab630632994ca09b0fef9cd23281188f427fe0979255
+size 590845

preprocessor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cc23657bb8f71c44eb4d8598bcdcaa98b9941b8a6a62277d72acd7f4947e35be
+size 6451