Upload folder using huggingface_hub

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

.gradio/certificate.pem

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+-----END CERTIFICATE-----

.gradio/flagged/dataset1.csv

@@ -0,0 +1,7 @@
+script,Analysis Results,Emotional and Musical Arc,timestamp
+"A year from now we'll all be gone, all our friends will move away and they're going to better places.","**Line 1:**
+A year from now we'll all be gone, all our friends will move away and they're going to better places.
+Sentiment: POSITIVE (Score: 0.99)
+Scene Description: A warm glow fills the air, carrying a sense of joy and peace.
+Music Cue: Soft, uplifting melody with bright piano notes.
+",.gradio/flagged/Emotional and Musical Arc/44badc0663277c7b4461/emotional_music_arc.png,2024-11-07 19:37:06.048168

README.md

@@ -1,12 +1,6 @@
 ---
 title: VisionFlow
-emoji: 🚀
-colorFrom: pink
-colorTo: pink
+app_file: script_analyzer.py
 sdk: gradio
 sdk_version: 5.5.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

script_analyzer.py

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+import gradio as gr
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, AutoModelForCausalLM
+import matplotlib.pyplot as plt
+
+# Load advanced sentiment analysis model
+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")
+
+# Helper function to generate text using GPT-Neo or GPT-J
+def generate_text(prompt):
+    inputs = tokenizer_gpt(prompt, return_tensors="pt", truncation=True, max_length=50)
+    outputs = model_gpt.generate(inputs["input_ids"], max_length=50, num_return_sequences=1, no_repeat_ngram_size=2)
+    generated_text = tokenizer_gpt.decode(outputs[0], skip_special_tokens=True)
+    return generated_text.strip()
+
+# Function to analyze each line of the script
+def analyze_script(script):
+    global all_scores, descriptions, music_cues
+    lines = script.strip().split("\n")
+    all_scores = []
+    descriptions = []
+    music_cues = []
+    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}",
+                "Sentiment": f"{sentiment} (Score: {round(score, 2)})",
+                "Description Suggestion": description,
+                "Music Cue": music_cue
+            }
+        )
+    
+    # Generate the emotional arc graph for the entire script
+    graph_path = generate_script_graph()
+    return analysis_results, graph_path
+
+# Generate the emotional arc graph with music cues for the entire script
+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)
+    
+    plt.title('Emotional and Musical Arc for Entire Script')
+    plt.xlabel('Script Lines (Accumulative)')
+    plt.ylabel('Sentiment Intensity')
+    plt.legend()
+    plt.tight_layout()
+    
+    # Save plot as image file
+    plot_path = "script_emotional_arc.png"
+    plt.savefig(plot_path)
+    plt.close()
+    return plot_path
+
+# Custom Gradio component to display dashboard results with icons
+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>
+        """
+    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.")
+    
+    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")
+    
+    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)
+        return dashboard_content, graph_path
+    
+    display_dashboard_button.click(display_dashboard, inputs=script_input, outputs=[output_dashboard, output_graph])
+
+# Launch the Gradio app with sharing enabled
+interface.launch(share=True)