Update app.py

app.py

@@ -5,11 +5,8 @@ import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from textblob import TextBlob
 
-
-
-
 # Load the dataset from the local file
-data = pd.read_csv('twitter_dataset.csv').head(1000)
+data = pd.read_csv('twitter_dataset.csv')
 
 # Calculate sentiment polarity and popularity
 data['Sentiment'] = data['Text'].apply(lambda x: TextBlob(x).sentiment.polarity)
@@ -39,7 +36,7 @@ data['Fake_News_Prediction'] = predictions
 data['Credibility'] = data['Fake_News_Prediction'].apply(lambda x: 1 if x == 1 else -1)
 
 # Define the prediction and recommendation function
-def predict_and_recommend(title, text, visibility_weight, sentiment_weight, popularity_weight):
+def predict_and_recommend(visibility_weight, sentiment_weight, popularity_weight):
     # Adjust weights and calculate the final score
     total_weight = visibility_weight + sentiment_weight + popularity_weight
     visibility_weight /= total_weight
@@ -56,21 +53,22 @@ def predict_and_recommend(title, text, visibility_weight, sentiment_weight, popu
     top_100_data = data.nlargest(100, 'User_Final_Visibility_Score')
     recommended_data = top_100_data.sample(10)
 
-    return recommended_data[['Text', 'User_Final_Visibility_Score']]
+    # Format output with empty lines between tweets
+    output = "\n\n".join(f"**Tweet**: {row['Text']}\n**Score**: {row['User_Final_Visibility_Score']:.2f}"
+                         for _, row in recommended_data.iterrows())
+    return output
 
 # Set up Gradio interface
 iface = gr.Interface(
     fn=predict_and_recommend,
     inputs=[
-        gr.Textbox(label="Title"),
-        gr.Textbox(label="Text", lines=10),
         gr.Slider(0, 1, 0.5, label="Visibility Weight"),
         gr.Slider(0, 1, 0.3, label="Sentiment Weight"),
         gr.Slider(0, 1, 0.2, label="Popularity Weight")
     ],
-    outputs="dataframe",
+    outputs="markdown",
     title="Customizable Fake News Recommendation System",
     description="Adjust weights to receive customized tweet recommendations based on visibility, sentiment, and popularity."
 )
 
-iface.launch()
+iface.launch()