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app.py

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+import pandas as pd
+import numpy as np
+import nltk
+import re
+import string
+from nltk.corpus import stopwords
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import PassiveAggressiveClassifier
+import gradio as gr
+
+# Download NLTK resources if not already downloaded
+nltk.download('stopwords')
+nltk.download('punkt')
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+
+# Load the dataset
+df = pd.read_csv('/path/to/your/disaster_tweets.csv')  # Update path to your CSV file
+
+# Data preprocessing
+def clean_tweet(txt):
+    txt = txt.lower()
+    words = nltk.word_tokenize(txt)
+    stop = set(stopwords.words('english'))
+    words = [word for word in words if word not in stop]
+    words = ' '.join(words)
+    txt = re.sub('[^a-z]', ' ', words)
+    return txt
+
+df['clean_text'] = df['text'].apply(clean_tweet)
+
+# Split data into features and target
+X = df['clean_text']
+y = df['target']
+
+# Splitting the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=0)
+
+# TF-IDF Vectorization
+tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.8, ngram_range=(1, 3))
+tfidf_train = tfidf_vectorizer.fit_transform(X_train)
+tfidf_test = tfidf_vectorizer.transform(X_test)
+
+# Model training
+passive_aggressive = PassiveAggressiveClassifier()
+passive_aggressive.fit(tfidf_train, y_train)
+
+# Function for making predictions
+def predict_disaster_tweets(text):
+    cleaned_text = clean_tweet(text)
+    tfidf_text = tfidf_vectorizer.transform([cleaned_text])
+    prediction = passive_aggressive.predict(tfidf_text)[0]
+    return "Disaster Tweet" if prediction == 1 else "Normal Tweet"
+
+# Gradio Interface setup
+iface = gr.Interface(
+    fn=predict_disaster_tweets,
+    inputs=gr.Textbox(label="Enter Tweet Text", placeholder="Type here..."),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Disaster Tweet Classifier",
+    description="Predict whether a tweet indicates a disaster or not."
+)
+
+iface.launch(share=True)