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Readme.md

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+# 📊 Sentiment Analysis with Gradio
+
+This project demonstrates **Sentiment Analysis** using a dataset of social media posts.  
+The goal is to classify text into **Positive, Negative, or Neutral** sentiments using Natural Language Processing (NLP) techniques and deploy the model with a **Gradio interactive web app**.
+
+---
+
+## 🚀 Features
+- Text preprocessing (cleaning, tokenization, stopword removal)
+- Sentiment classification using **TF-IDF + Logistic Regression**
+- Interactive **Gradio app** for real-time predictions
+- Easy deployment and sharing via Colab or GitHub
+
+---
+
+## 📂 Project Structure

Requirements.txt

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+pandas
+scikit-learn
+nltk
+gradio
+joblib

app.py

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+
+import gradio as gr
+import joblib
+import re
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+
+# Load saved model and vectorizer
+model = joblib.load("sentiment_model.pkl")
+vectorizer = joblib.load("tfidf_vectorizer.pkl")
+
+nltk.download('punkt')
+nltk.download('stopwords')
+
+def preprocess(text):
+    text = str(text).lower()
+    text = re.sub(r'[^a-z\s]', '', text)
+    tokens = word_tokenize(text)
+    tokens = [t for t in tokens if t not in stopwords.words('english')]
+    return ' '.join(tokens)
+
+def predict_sentiment(text):
+    processed = preprocess(text)
+    vectorized = vectorizer.transform([processed])
+    prediction = model.predict(vectorized)[0]
+    return prediction
+
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="label",
+    title="Sentiment Analysis App",
+    description="Classify text as Positive, Negative, or Neutral"
+)
+
+demo.launch()

sentiment_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8993f3518cb64af77edc4170dfc9c1cd8d54dcaafa7cbe6b243fd38ca8175967
+size 31711

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ce96ea918674ec6ed82d2896219515ff4bd0649a6f1772f914fe8066160bb292
+size 25819