Upload 6 files

MultiOutput_NB.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b500848f0069fdcdffff29884f97a0617b6d0b2cf7b41be0710a9353bb9078c9
+size 4163656

app.py

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+import streamlit as st
+import pickle
+import re
+from scipy.sparse import hstack
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import LabelEncoder
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer, WordNetLemmatizer
+import nltk
+import numpy as np
+
+# Ensure required NLTK data is downloaded
+nltk.download('punkt')
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+# Load pre-trained models and encoders
+with open("MultiOutput_NB.pkl", "rb") as file:
+    nb_model = pickle.load(file)
+
+with open("MultiOutput_RF.pkl", "rb") as file:
+    rf_model = pickle.load(file)
+
+with open("vectorizer.pkl", "rb") as file:
+    vectorizer = pickle.load(file)
+
+with open("category_encoder.pkl", "rb") as file:
+    label_encoder_category = pickle.load(file)
+
+with open("subcategory_encoder.pkl", "rb") as file:
+    label_encoder_subcategory = pickle.load(file)
+
+# Load custom stopwords
+def load_custom_stopwords(file_path):
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            words = [line.strip() for line in file if line.strip()]
+        return set(words)
+    except FileNotFoundError:
+        st.error(f"Stopwords file '{file_path}' not found.")
+        return set()
+
+# Function to preprocess input text
+def preprocess_text(text):
+    # Load stopwords
+    file_path = 'stop_hinglish.txt'
+    custom_stopwords = load_custom_stopwords(file_path)
+    eng_stopwords = set(stopwords.words('english'))
+    hin_stopwords = set(stopwords.words('hinglish'))
+    combined_stopwords = eng_stopwords.union(hin_stopwords).union(custom_stopwords)
+
+    # Preprocessing steps
+    text = re.sub(r'[^a-zA-Z\s]', '', text)  # Remove special characters
+    text = text.lower()  # Convert to lowercase
+    words = word_tokenize(text)  # Tokenize text
+    words = [word for word in words if word not in combined_stopwords]  # Remove stopwords
+
+    # Stemming and lemmatization
+    stemmer = PorterStemmer()
+    lemmatizer = WordNetLemmatizer()
+    words = [lemmatizer.lemmatize(stemmer.stem(word)) for word in words]
+
+    # Join the words back into a single string
+    processed_text = ' '.join(words)
+    return processed_text
+
+# Streamlit app
+st.title("Crime Category & Subcategory Classification")
+st.write("Enter the crime description below to classify its category and sub-category.")
+
+# Model selection dropdown
+model_choice = st.selectbox(
+    "Choose the classification model:",
+    ["Naive Bayes", "Random Forest"]
+)
+
+# Input area
+user_input = st.text_area("Crime Description:")
+
+if st.button("Classify"):
+    if user_input.strip():  # Check if input is not empty
+        # Preprocess the input
+        cleaned_text = preprocess_text(user_input)
+        st.write("Cleaned text:", cleaned_text)
+
+        # Vectorize the input text
+        vectorized_text = vectorizer.transform([cleaned_text])
+
+        # Additional features placeholder (if needed in the sparse matrix)
+        additional_features = [[0, 0]]  # Placeholder for any numeric features, replace as necessary
+
+        # Combine vectorized text with additional features
+        combined_features = hstack([vectorized_text, additional_features])
+
+        # Select the model based on user choice
+        if model_choice == "Naive Bayes":
+            model = nb_model
+        elif model_choice == "Random Forest":
+            model = rf_model
+        else:
+            st.error("Invalid model choice.")
+            st.stop()
+
+        # Predict using the selected model
+        predictions = model.predict(combined_features)
+
+        try:
+            # Extract indices if predictions are probabilities or multi-output
+            if predictions.ndim == 3:  # Multi-output with separate arrays
+                category_index = np.argmax(predictions[0])  # First output
+                subcategory_index = np.argmax(predictions[1])  # Second output
+            elif predictions.ndim == 2:  # Multi-output as a single array
+                category_index = int(predictions[0][0])
+                subcategory_index = int(predictions[0][1])
+            else:
+                st.error("Unexpected model output shape. Please check your model.")
+                st.stop()
+
+            # Decode predictions
+            predicted_category = label_encoder_category.inverse_transform([category_index])[0]
+            predicted_subcategory = label_encoder_subcategory.inverse_transform([subcategory_index])[0]
+
+            # Display results
+            st.success("Classification Results:")
+            st.write(f"**Category:** {predicted_category}")
+            st.write(f"**Sub-Category:** {predicted_subcategory}")
+
+        except Exception as e:
+            st.error(f"Error decoding predictions: {e}")
+    else:
+        st.error("Please enter a valid crime description.")

category_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d6f1b04f9bbdb91519fa99444f601b530761b15b01d2f53e7727567973db9e4d
+size 731

stop_hinglish.txt

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+a
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+aapne
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+aati
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+aise
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+all
+allow
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+along
+already
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+although
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+am
+among
+amongst
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+and
+andar
+another
+any
+anybody
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+anything
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+certainly
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+clearly
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subcategory_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e4d6681741bd155873a14009177a325a6019d117a2d62cc5a04390b13223b29
+size 1304

vectorizer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1960e3d951ee705ae97a7e65b130093eb0d4f152e6f70e55bcd4ed60244b406e
+size 158081