Update demo.py

demo.py

@@ -5,6 +5,7 @@ from huggingface_hub import hf_hub_download
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+
 import pickle
 import numpy as np
 from tensorflow.keras.models import load_model
@@ -19,7 +20,7 @@ vectorizer_path = hf_hub_download(repo_id=svm_repo_id, filename="vectorizer.pkl"
 with open(vectorizer_path, "rb") as f:
     vectorizer = pickle.load(f)
 
-lstm_repo_id = "arjahojnik/LSTM-sentiment-model"
+lstm_repo_id = "HighFive-OPJ/Deep_Learning"
 lstm_model_path = hf_hub_download(repo_id=lstm_repo_id, filename="LSTM_model.h5")
 lstm_model = load_model(lstm_model_path)
 lstm_tokenizer_path = hf_hub_download(repo_id=lstm_repo_id, filename="my_tokenizer.pkl")
@@ -47,7 +48,7 @@ def predict_with_lstm(text):
     padded_seq = pad_sequences(seq, maxlen=200)
     probs = lstm_model.predict(padded_seq)
     predicted_class = np.argmax(probs, axis=1)[0]
-    return int(predicted_class + 1)
+    return int(predicted_class)
 
 def predict_with_bert(text):
     inputs = bert_tokenizer([text], padding=True, truncation=True, max_length=512, return_tensors="pt").to(device)
@@ -55,7 +56,13 @@ def predict_with_bert(text):
         outputs = bert_model(**inputs)
         logits = outputs.logits
         predictions = logits.argmax(axis=-1).cpu().numpy()
-    return int(predictions[0] + 1)
+    bert_score = int(predictions[0])
+    if bert_score <= 2:
+        return 0
+    elif bert_score == 3:
+        return 1
+    else:
+        return 2
 
 def analyze_sentiment(text):
     results = {
@@ -65,7 +72,7 @@ def analyze_sentiment(text):
     }
     scores = list(results.values())
     average = np.mean(scores)
-    stats = f"Average Score: {average:.2f}\n"
+    stats = f"Average Score (0=Neg,1=Neu,2=Pos): {average:.2f}\n"
     return (
         convert_to_stars(results["SVM"]),
         convert_to_stars(results["LSTM"]),
@@ -74,7 +81,10 @@ def analyze_sentiment(text):
     )
 
 def convert_to_stars(score):
-    return "★" * score + "☆" * (5 - score)
+    # Map 0->1 star, 1->3 stars, 2->5 stars
+    star_map = {0: 1, 1: 3, 2: 5}
+    stars = star_map.get(score, 3)  
+    return "★" * stars + "☆" * (5 - stars)
 
 def process_input(text):
     if not text.strip():