Update app.py

app.py

@@ -1,84 +1,19 @@
-from google.colab import drive
-from sklearn.model_selection import train_test_split
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn import svm
-import pandas as pd
-import numpy as np
-import random
 import gradio as gr
+from joblib import load
 
-# Mount Google Drive to access your dataset
-drive.mount("/content/gdrive")
+# Define function to load SVM model
+def load_svm_model(model_url):
+    return load(model_url)
 
-# Define classes for reviews and review container
-class Review:
-    def __init__(self, text, score):
-        self.text = text
-        self.score = score
-        self.sentiment, self.probability = self.get_sentiment()
+# Load the SVM model from Hugging Face
+svm_model = load_svm_model("https://huggingface.co/ankitdotpy/SVM_model_by_Group12")
 
-    def get_sentiment(self):
-        if self.score <= 2:
-            return "NEGATIVE", 1.0 if self.score == 0 else 0.8
-        elif self.score == 3:
-            return "NEUTRAL", 0.5
-        else: #Score of 4 or 5
-            return "POSITIVE", 1.0 if self.score == 5 else 0.8
-
-class ReviewContainer:
-    def __init__(self, reviews):
-        self.reviews = reviews
-
-    def get_text(self):
-        return [x.text for x in self.reviews]
-
-    def get_sentiment(self):
-        return [x.sentiment for x in self.reviews]
-
-    def get_probability(self):
-        return [x.probability for x in self.reviews]
-
-    def evenly_distribute(self):
-        negative = list(filter(lambda x: x.sentiment == "NEGATIVE", self.reviews))
-        positive = list(filter(lambda x: x.sentiment == "POSITIVE", self.reviews))
-        positive_shrunk = positive[:len(negative)]
-        self.reviews = negative + positive_shrunk
-        random.shuffle(self.reviews)
-
-# Load the dataset
-df = pd.read_json('/content/gdrive/My Drive/Datasets/Books_subset3.json', lines=True)
-
-# Create Review objects
-reviews = [Review(x, y) for x, y in zip(df['review_body'], df['star_rating'])]
-
-# Split data into train and test sets
-train,test = train_test_split(reviews, test_size=0.25, random_state=42)
-
-# Initialize ReviewContainer objects
-train_container = ReviewContainer(train)
-test_container = ReviewContainer(test)
-
-# Evenly distribute the data
-train_container.evenly_distribute()
-train_x = train_container.get_text()
-train_y = train_container.get_sentiment()
-
-test_container.evenly_distribute()
-test_x = test_container.get_text()
-test_y = test_container.get_sentiment()
-
-# Vectorize the data
-vectorizer = TfidfVectorizer()
-train_x_vectors = vectorizer.fit_transform(train_x)
-test_x_vectors = vectorizer.transform(test_x)
-
-# Train SVM model
-clf_svm = svm.SVC(kernel='linear', probability=True)
-clf_svm.fit(train_x_vectors, train_y)
-
-# Define function for Gradio interface
+# Define function for Gradio interface using the loaded model
 def predict_sentiment(text):
-    # Vectorize the input text
+    # Load the SVM model
+    clf_svm = svm_model
+
+    # Vectorize the input text (assuming you already have the vectorizer)
     text_vector = vectorizer.transform([text])
 
     # Predict sentiment
@@ -102,10 +37,4 @@ def predict_sentiment(text):
 iface = gr.Interface(
     fn=predict_sentiment,
     inputs=gr.Textbox(placeholder="Enter Text", lines=10, label="Enter your text here:"),
-    outputs=gr.Textbox(label="Sentiment"),
-    title="Sentiment Analysis Developed by Group-12(Ankit, Akshat, Gautam, Pritish) with ♥ from RCC Institute of Information Technology.",
-    description="Enter text and predict sentiment"
-)
-
-# Launch the interface
-iface.launch(inline=False)
+    outputs