Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import gradio as gr
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import accuracy_score
+
+# Step 1: Load and preprocess the data
+# Example data loading (replace this with your actual data)
+data = pd.read_csv("/content/flipkart_reviews.csv")
+
+# Preprocessing function
+def preprocess_text(text):
+    text = text.lower()
+    text = ''.join([char for char in text if char.isalnum() or char.isspace()])
+    return text
+
+data['Review'] = data['Review'].apply(preprocess_text)
+
+# Step 2: Feature Engineering
+tfidf = TfidfVectorizer()
+X_text = tfidf.fit_transform(data['Review'])
+
+# Combine TF-IDF features with the rating
+X = np.hstack((X_text.toarray(), data['Rating'].values.reshape(-1, 1)))
+
+# Define the five sentiment categories
+def categorize_sentiment(rating):
+    if rating == 1:
+        return 'Worst 😡'
+    elif rating == 2:
+        return 'Poor 😟'
+    elif rating == 3:
+        return 'Good 🙂'
+    elif rating == 4:
+        return 'Better 😊'
+    else:
+        return 'Best 😍'
+
+y = data['Rating'].apply(categorize_sentiment)
+
+# Step 3: Model Training
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+model = LogisticRegression()
+model.fit(X_train, y_train)
+
+# Evaluate the model
+y_pred = model.predict(X_test)
+print("Accuracy:", accuracy_score(y_test, y_pred))
+
+# Step 4: Create Gradio Interface
+def predict_sentiment(review, rating):
+    review = preprocess_text(review)
+    review_tfidf = tfidf.transform([review])
+    features = np.hstack((review_tfidf.toarray(), [[rating]]))
+    sentiment = model.predict(features)
+    return sentiment[0]
+
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=[gr.Textbox(lines=2, placeholder="Enter your review here..."), gr.Slider(1, 5, step=1, label="Rating")],
+    outputs="text",
+    title="Flipkart Review Sentiment Analysis",
+    description="Enter your Flipkart review and rating to predict its sentiment."
+)
+
+# Launch the interface
+interface.launch(inline=False)