Update app.py

app.py

@@ -359,7 +359,7 @@ demo = gr.Interface(
 
 demo.launch()
 '''
-'''
+
 import gradio as gr
 from transformers import TFBertForSequenceClassification, BertTokenizer
 import tensorflow as tf
@@ -534,122 +534,11 @@ demo = gr.TabbedInterface(
 )
 
 demo.launch()
-'''
-import gradio as gr
-from transformers import TFBertForSequenceClassification, BertTokenizer
-import tensorflow as tf
-from sklearn.metrics import accuracy_score, f1_score, classification_report
-import numpy as np
-
-# Load models
-model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
-tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
-LABELS = {0: "Neutral", 1: "Positive", 2: "Negative"}
-
-# Load fallback model
-fallback_model_name = "cardiffnlp/twitter-roberta-base-sentiment"
-fallback_tokenizer = AutoTokenizer.from_pretrained(fallback_model_name)
-fallback_model = AutoModelForSequenceClassification.from_pretrained(fallback_model_name)
-
-def analyze_text(text, true_label=None):
-    try:
-        # Main model prediction
-        inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
-        outputs = model(inputs)
-        probs = tf.nn.softmax(outputs.logits, axis=1)
-        main_pred = LABELS[tf.argmax(probs, axis=1).numpy()[0]]
-        
-        # Fallback model prediction
-        fallback_inputs = fallback_tokenizer(text, return_tensors='pt', truncation=True, padding=True)
-        with torch.no_grad():
-            fallback_outputs = fallback_model(**fallback_inputs)
-        fallback_scores = softmax(fallback_outputs.logits.numpy()[0])
-        fallback_pred = ['Negative', 'Neutral', 'Positive'][np.argmax(fallback_scores)]
-
-        # Initialize results
-        result = f"""Main Model Prediction: {main_pred}
-Fallback Model Prediction: {fallback_pred}"""
-
-        # Calculate metrics if true label provided
-        if true_label:
-            # Convert labels to numerical format
-            label_map = {v: k for k, v in LABELS.items()}
-            y_true = [label_map[true_label]]
-            
-            # Main model metrics
-            y_pred_main = [label_map[main_pred]]
-            main_acc = accuracy_score(y_true, y_pred_main)
-            main_f1 = f1_score(y_true, y_pred_main, average='weighted')
-            
-            # Fallback model metrics
-            fallback_label_map = {'Negative': 2, 'Neutral': 0, 'Positive': 1}
-            y_pred_fallback = [fallback_label_map[fallback_pred]]
-            fallback_acc = accuracy_score(y_true, y_pred_fallback)
-            fallback_f1 = f1_score(y_true, y_pred_fallback, average='weighted')
-
-            # Classification report
-            report = classification_report(
-                y_true, y_pred_main,
-                target_names=LABELS.values(),
-                output_dict=True
-            )
-
-            # Format metrics
-            metrics = f"""
-\n\nPERFORMANCE METRICS (Single Sample):
-------------------------------------
-Main Model:
-Accuracy: {main_acc:.4f}
-F1 Score: {main_f1:.4f}
-
-Fallback Model:
-Accuracy: {fallback_acc:.4f}
-F1 Score: {fallback_f1:.4f}
-
-Classification Report:
-{classification_report(y_true, y_pred_main, target_names=LABELS.values())}
-"""
-
-            result += metrics
-
-        return result
 
-    except Exception as e:
-        return f"Error: {str(e)}"
 
-# Gradio interface
-demo = gr.Interface(
-    fn=analyze_text,
-    inputs=[
-        gr.Textbox(
-            label="Input Text",
-            placeholder="Enter text to analyze...",
-            lines=4
-        ),
-        gr.Dropdown(
-            label="True Label (optional, for metrics)",
-            choices=list(LABELS.values()),
-            value=None
-        )
-    ],
-    outputs=gr.Textbox(
-        label="Analysis Results",
-        lines=10
-    ),
-    title="Sentiment Analysis with Performance Metrics",
-    description="""Enter text and optionally select true label to generate:
-    - Predictions from both models
-    - Accuracy scores
-    - F1 scores
-    - Classification report""",
-    examples=[
-        ["I absolutely love this new feature!", "Positive"],
-        ["This is the worst experience ever!", "Negative"],
-        ["The product seems okay, nothing special.", "Neutral"]
-    ]
-)
+ 
+  
 
-demo.launch()