Update app.py

app.py

@@ -3,14 +3,9 @@ from transformers import pipeline
 import os
 
 # --- Model Loading ---
-# Using the model ID you've been working with, which is fine-tuned on an English dataset (IMDB).
-# It's crucial that this model is indeed fine-tuned for sentiment analysis in English.
 MODEL_ID = "Light-Dav/sentiment-analysis-full-project"
 
 try:
-    # Attempt to load the pipeline. This needs to be outside the function for efficiency.
-    # Using top_k=None to get all scores (equivalent to return_all_scores=True in older versions).
-    # This also addresses the UserWarning about `return_all_scores` deprecation.
     sentiment_analyzer = pipeline("sentiment-analysis", model=MODEL_ID, top_k=None)
     model_loaded_successfully = True
 except Exception as e:
@@ -123,23 +118,16 @@ def analyze_sentiment(text):
         }
 
     try:
-        # The pipeline returns a list of lists of dictionaries if top_k=None
-        # e.g., [[{'label': 'LABEL_0', 'score': 0.9}, {'label': 'LABEL_1', 'score': 0.05}, ...]]
-        results = sentiment_analyzer(text)[0] # Get the first (and only) list of results
+        results = sentiment_analyzer(text)[0] 
 
-        # Sort results by score in descending order
         results_sorted = sorted(results, key=lambda x: x['score'], reverse=True)
 
-        # Get the top sentiment
         top_sentiment = results_sorted[0]
         label = top_sentiment['label']
         score = top_sentiment['score']
 
-        # Format for Gradio Label component (Dictionary {label: score})
-        # This is for the 'Confidence Scores' output
         confidence_scores_output = {item['label']: item['score'] for item in results}
 
-        # Interpret sentiment for the 'Overall Sentiment' output
         overall_sentiment_display = interpret_sentiment(label, score)
 
         return {
@@ -155,12 +143,12 @@ def analyze_sentiment(text):
         }
 
 # --- Gradio Interface ---
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo: # Using gr.Blocks for more layout control
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
     gr.Markdown("# ✨ Sentiment Spark ✨")
     gr.Markdown("---")
     gr.Markdown("### Uncover the emotional tone of your English text instantly!")
 
-    with gr.Row(): # Horizontal layout for input and outputs
+    with gr.Row():
         with gr.Column(scale=2):
             text_input = gr.Textbox(
                 lines=7,
@@ -169,7 +157,15 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo: # Using gr.Block
                 interactive=True,
                 value="This movie was absolutely brilliant! A masterpiece of storytelling and emotion."
             )
-            analyze_btn = gr.Button("Analyze Sentiment", variant="primary") # The main action button
+            analyze_btn = gr.Button("Analyze Sentiment", variant="primary")
+
+            # --- Define outputs BEFORE gr.Examples ---
+            # These variables need to exist before they are referenced in gr.Examples
+            gr.Markdown("## 📈 Analysis Results 📉")
+            overall_sentiment_output = gr.HTML(label="Overall Sentiment")
+            confidence_scores_output = gr.Label(num_top_classes=3, label="Confidence Scores")
+            raw_output = gr.JSON(label="Raw Model Output", visible=False)
+            # ----------------------------------------
 
             gr.Markdown("---")
             gr.Markdown("### Try some examples:")
@@ -183,29 +179,19 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo: # Using gr.Block
                     ["The meeting concluded without any major decisions."]
                 ],
                 inputs=text_input,
-                fn=analyze_sentiment, # <-- CORRECTED: Function to run for examples
-                outputs=[overall_sentiment_output, confidence_scores_output, raw_output], # <-- CORRECTED: Outputs for examples
+                fn=analyze_sentiment, # Function to run for examples
+                outputs=[overall_sentiment_output, confidence_scores_output, raw_output], # Outputs for examples
                 cache_examples=True # Caching examples for faster loading
             )
 
-        with gr.Column(scale=3):
-            gr.Markdown("## 📈 Analysis Results 📉")
-            # Using gr.HTML for "Overall Sentiment" to allow custom CSS and rich content
-            overall_sentiment_output = gr.HTML(label="Overall Sentiment")
-            # Using gr.Label for "Confidence Scores" as it's designed for classification outputs
-            confidence_scores_output = gr.Label(num_top_classes=3, label="Confidence Scores")
-            # Using gr.JSON for raw output, useful for debugging, but hidden by default
-            raw_output = gr.JSON(label="Raw Model Output", visible=False)
 
     # --- Event Listeners ---
-    # Trigger analysis when text input changes (optional, can be resource intensive if live=True)
     text_input.change(
         fn=analyze_sentiment,
         inputs=text_input,
         outputs=[overall_sentiment_output, confidence_scores_output, raw_output],
-        # live=True # Uncomment for live updates as user types (can be resource intensive)
+        # live=True
     )
-    # Trigger analysis when the button is clicked
     analyze_btn.click(
         fn=analyze_sentiment,
         inputs=text_input,