Explicitly add [PAD] token, add Geist icons and styles

script_analyzer.py

@@ -2,27 +2,30 @@ import gradio as gr
 from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, AutoModelForCausalLM
 import matplotlib.pyplot as plt
 
-# Load advanced sentiment analysis model
+# Load sentiment and generation models as previously set up
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 sentiment_classifier = pipeline("sentiment-analysis", model=model_sentiment, tokenizer=tokenizer_sentiment)
 
-# Load open-source language model for description and music cue generation (GPT-J or GPT-Neo)
 tokenizer_gpt = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
 model_gpt = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
 
-# Updated generate_text function with attention_mask and pad_token_id
+# Updated generate_text function with padding token fix
 def generate_text(prompt):
+    # Add '[PAD]' as a padding token if not set
+    if tokenizer_gpt.pad_token is None:
+        tokenizer_gpt.add_special_tokens({'pad_token': '[PAD]'})
+
     # Prepare the input tensors with attention_mask and padding
     inputs = tokenizer_gpt(prompt, return_tensors="pt", padding=True, truncation=True, max_length=50)
     # Generate text using max_new_tokens instead of max_length
     outputs = model_gpt.generate(
         inputs["input_ids"],
         attention_mask=inputs["attention_mask"],
-        max_new_tokens=50,  # Controls the new tokens generated beyond input length
+        max_new_tokens=50,
         num_return_sequences=1,
         no_repeat_ngram_size=2,
-        pad_token_id=tokenizer_gpt.eos_token_id  # Sets padding to eos_token_id to prevent issues
+        pad_token_id=tokenizer_gpt.pad_token_id  # Ensure pad_token_id is set
     )
     generated_text = tokenizer_gpt.decode(outputs[0], skip_special_tokens=True)
     return generated_text.strip()
@@ -37,25 +40,20 @@ def analyze_script(script):
     analysis_results = []
     
     for i, line in enumerate(lines):
-        # Perform sentiment analysis with advanced model
         result = sentiment_classifier(line)[0]
         sentiment = result['label']
         score = result['score']
         
-        # Generate a detailed scene description based on sentiment and line content
         description_prompt = f"Describe a scene with the sentiment '{sentiment}' for the line: '{line}'"
         description = generate_text(description_prompt)
         
-        # Generate a specific music cue suggestion based on sentiment
         music_cue_prompt = f"Suggest music elements (like tempo, key, and instrumentation) that would fit a scene with the sentiment '{sentiment}': '{line}'"
         music_cue = generate_text(music_cue_prompt)
         
-        # Append data for display and graph
         all_scores.append(score)
         descriptions.append(description)
         music_cues.append(music_cue)
         
-        # Format analysis results
         analysis_results.append(
             {
                 "Line": f"Line {i + 1}: {line}",
@@ -65,7 +63,6 @@ def analyze_script(script):
             }
         )
     
-    # Generate the emotional arc graph for the entire script
     graph_path = generate_script_graph()
     return analysis_results, graph_path
 
@@ -74,7 +71,6 @@ def generate_script_graph():
     plt.figure(figsize=(12, 6))
     plt.plot(all_scores, marker='o', linestyle='-', color='b', label='Sentiment Intensity')
     
-    # Add text labels for music cues along the graph
     for i, score in enumerate(all_scores):
         plt.text(i, score, music_cues[i], fontsize=8, ha='right', rotation=45)
     
@@ -84,7 +80,6 @@ def generate_script_graph():
     plt.legend()
     plt.tight_layout()
     
-    # Save plot as image file
     plot_path = "script_emotional_arc.png"
     plt.savefig(plot_path)
     plt.close()
@@ -95,19 +90,19 @@ def format_dashboard(results):
     formatted_results = ""
     for result in results:
         formatted_results += f"""
-        <div style="border:1px solid #ddd; padding:10px; margin-bottom:10px; border-radius:5px;">
-            <p>🎬 <strong>{result['Line']}</strong></p>
-            <p>📊 <strong>Sentiment:</strong> {result['Sentiment']}</p>
-            <p>💡 <strong>Description Suggestion:</strong> {result['Description Suggestion']}</p>
-            <p>🎶 <strong>Music Cue:</strong> {result['Music Cue']}</p>
+        <div class="dashboard-box">
+            <p><img src="https://geist-ui.dev/icons/activity.svg" class="dashboard-icon" alt="Line icon"> <strong>{result['Line']}</strong></p>
+            <p><img src="https://geist-ui.dev/icons/bar-chart.svg" class="dashboard-icon" alt="Sentiment icon"> <strong>Sentiment:</strong> {result['Sentiment']}</p>
+            <p><img src="https://geist-ui.dev/icons/eye.svg" class="dashboard-icon" alt="Description icon"> <strong>Description Suggestion:</strong> {result['Description Suggestion']}</p>
+            <p><img src="https://geist-ui.dev/icons/music.svg" class="dashboard-icon" alt="Music Cue icon"> <strong>Music Cue:</strong> {result['Music Cue']}</p>
         </div>
         """
     return formatted_results
 
 # Gradio interface to analyze script and display the dashboard
-with gr.Blocks() as interface:
-    gr.Markdown("## Script Sentiment and Music Cue Analyzer")
-    gr.Markdown("Enter your script line-by-line, and this tool will analyze sentiment, generate scene descriptions, suggest music cues, and show an emotional and musical arc.")
+with gr.Blocks(css="custom.css") as interface:
+    gr.Markdown("## Script Sentiment and Music Cue Analyzer", elem_id="title")
+    gr.Markdown("Enter your script line-by-line, and this tool will analyze sentiment, generate scene descriptions, suggest music cues, and show an emotional and musical arc.", elem_id="description")
     
     script_input = gr.Textbox(lines=10, placeholder="Enter your script here, one line per thought or dialogue.", label="Script")
     display_dashboard_button = gr.Button("Analyze Script")
@@ -115,7 +110,6 @@ with gr.Blocks() as interface:
     output_dashboard = gr.HTML(label="Dashboard Results")
     output_graph = gr.Image(label="Emotional and Musical Arc for Entire Script")
 
-    # Display dashboard functionality
     def display_dashboard(script):
         analysis_results, graph_path = analyze_script(script)
         dashboard_content = format_dashboard(analysis_results)
@@ -123,5 +117,4 @@ with gr.Blocks() as interface:
     
     display_dashboard_button.click(display_dashboard, inputs=script_input, outputs=[output_dashboard, output_graph])
 
-# Launch the Gradio app (no need for share=True in Hugging Face Spaces)
-interface.launch()
+interface.launch()