added the full app into the space and also changed the maximum and minimum value

app.py

@@ -1,57 +1,86 @@
 """
-Gradio Interface for Indonesian Court Document Summarization
-
-This is a conversion from Flask to Gradio for easier deployment on Hugging Face Spaces.
+Combined Gradio App - Indonesian AI Tools
+This is the MAIN FILE for Hugging Face Spaces deployment
 
 LEARNING NOTES:
-- Gradio automatically creates a web UI from function definitions
-- No need for HTML templates or route decorators
-- Input/output types define the UI components
+- Uses gr.TabbedInterface to combine multiple features
+- Each tab is a separate gr.Interface
+- This replaces your entire Flask app with one file
+- Hugging Face Spaces will automatically run this file
 """
 
 import gradio as gr
 import torch
+import tiktoken
 from pretrained_summarizer import create_summarizer
+from ml_model import GPTModel, generate_text_better, text_token_ids, token_text_ids
 
 # ============================================================================
-# Step 1: Initialize the model (same as Flask)
+# Initialize Device
 # ============================================================================
-print("Loading summarization model...")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
+print(f"🖥️  Using device: {device}")
 
+# ============================================================================
+# Load Models
+# ============================================================================
+
+# --- Summarization Model ---
+print("\n[1/2] Loading summarization model...")
 try:
     summarizer = create_summarizer("balanced")
-    print("✓ Summarization model loaded successfully!")
+    print("✓ Summarization model loaded!")
+    summarizer_available = True
 except Exception as e:
-    print(f"✗ Failed to load model: {e}")
-    raise
+    print(f"✗ Summarization model failed: {e}")
+    summarizer_available = False
+
+# --- Text Generation Model (Optional - may not fit in free tier) ---
+print("\n[2/2] Loading custom GPT model...")
+try:
+    checkpoint = torch.load('gpt_model_checkpoint.pth', map_location=device)
+    model = GPTModel(checkpoint['config'])
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.to(device)
+    model.eval()
+    tokenizer = tiktoken.get_encoding("gpt2")
+    print("✓ Custom GPT model loaded!")
+    gpt_available = True
+except FileNotFoundError:
+    print("✗ GPT model not found (gpt_model_checkpoint.pth)")
+    print("  Skipping text generation feature...")
+    gpt_available = False
+except Exception as e:
+    print(f"✗ GPT model failed: {e}")
+    gpt_available = False
+
+print("\n" + "="*60)
+print("🚀 Gradio App Ready!")
+print("="*60)
+print(f"✓ Summarization: {'Available' if summarizer_available else 'Unavailable'}")
+print(f"✓ Text Generation: {'Available' if gpt_available else 'Unavailable'}")
+print("="*60 + "\n")
 
 # ============================================================================
-# Step 2: Define the main function (replaces Flask route)
+# TAB 1: Court Document Summarization
 # ============================================================================
+
 def summarize_document(document, max_length, min_length, num_beams):
-    """
-    This function replaces your Flask /summarize endpoint.
+    """Summarize Indonesian court documents"""
 
-    Parameters match your Flask API, but return values are simpler
-    - No jsonify() needed
-    - Gradio handles the response automatically
-    """
+    if not summarizer_available:
+        return "❌ Summarization model is not available"
 
-    # Validation (same as Flask)
     if not document or not document.strip():
-        return "❌ Error: Please enter a document to summarize"
+        return "❌ Please enter a document to summarize"
 
     if max_length < min_length:
-        return "❌ Error: Max length must be greater than min length"
+        return "❌ Max length must be greater than min length"
 
-    # Cap max_length (same as Flask)
     if max_length > 1024:
         max_length = 1024
 
     try:
-        # Generate summary (same logic as Flask)
         summary = summarizer.summarize(
             document=document,
             max_length=int(max_length),
@@ -59,110 +88,198 @@ def summarize_document(document, max_length, min_length, num_beams):
             num_beams=int(num_beams)
         )
 
-        # Calculate statistics
         doc_words = len(document.split())
         summary_words = len(summary.split())
-        compression_ratio = round(summary_words / doc_words, 2) if doc_words > 0 else 0
+        compression = round(summary_words / doc_words, 2) if doc_words > 0 else 0
 
-        # Format output with statistics
         output = f"""📝 SUMMARY:
 {summary}
 
 📊 STATISTICS:
-• Document length: {doc_words} words
-• Summary length: {summary_words} words
-• Compression ratio: {compression_ratio}x
-• Device used: {device}
+• Original: {doc_words} words
+• Summary: {summary_words} words
+• Compression: {compression}x
+• Device: {device}
 """
         return output
 
     except Exception as e:
-        return f"❌ Error during summarization: {str(e)}"
+        return f"❌ Error: {str(e)}"
 
 
-# ============================================================================
-# Step 3: Create Gradio Interface
-# ============================================================================
-# This replaces your HTML templates and Flask routes
-demo = gr.Interface(
-    fn=summarize_document,  # The function to call
-
-    # Define inputs (replaces HTML form fields)
+summarize_interface = gr.Interface(
+    fn=summarize_document,
     inputs=[
         gr.Textbox(
             label="📄 Indonesian Court Document",
-            placeholder="Paste your court document text here...",
-            lines=10,
-            max_lines=20
-        ),
-        gr.Slider(
-            minimum=50,
-            maximum=1024,
-            value=200,
-            step=10,
-            label="Max Summary Length (words)",
-            info="Maximum length of the generated summary"
+            placeholder="Paste your court document here...",
+            lines=10
         ),
-        gr.Slider(
-            minimum=10,
-            maximum=100,
-            value=30,
-            step=5,
-            label="Min Summary Length (words)",
-            info="Minimum length of the generated summary"
-        ),
-        gr.Slider(
-            minimum=1,
-            maximum=10,
-            value=4,
-            step=1,
-            label="Num Beams",
-            info="Higher = better quality but slower (recommended: 4)"
-        )
+        gr.Slider(50, 1024, value=200, step=10, label="Max Length"),
+        gr.Slider(10, 500, value=30, step=10, label="Min Length"),
+        gr.Slider(1, 10, value=4, step=1, label="Num Beams")
     ],
+    outputs=gr.Textbox(label="✨ Summary", lines=15),
+    title="🏛️ Court Document Summarizer",
+    description="Summarize Indonesian court documents using AI",
+    examples=[
+        [
+            "Putusan Pengadilan Negeri Jakarta ini memutuskan bahwa terdakwa terbukti bersalah melakukan tindak pidana korupsi dengan merugikan negara sebesar 5 miliar rupiah. Majelis hakim mempertimbangkan bahwa terdakwa telah dengan sengaja memperkaya diri sendiri dan menyalahgunakan wewenang sebagai pejabat publik. Berdasarkan pertimbangan tersebut, terdakwa dijatuhi hukuman penjara selama 8 tahun dan denda 500 juta rupiah.",
+            200, 30, 4
+        ]
+    ]
+)
+
+# ============================================================================
+# TAB 2: Text Generation (if model available)
+# ============================================================================
+
+def generate_text(prompt, max_tokens, temperature, top_k):
+    """Generate text using custom GPT model"""
 
-    # Define output (replaces JSON response)
-    outputs=gr.Textbox(
-        label="✨ Generated Summary",
-        lines=15,
-        max_lines=25
-    ),
+    if not gpt_available:
+        return "❌ Text generation model is not available. This feature requires the 1.5GB model checkpoint which may not be included in this deployment."
 
-    # UI Configuration
-    title="🏛️ Indonesian Court Document Summarizer",
-    description="""
-    This tool uses a pre-trained AI model to summarize Indonesian court documents.
+    if not prompt or not prompt.strip():
+        return "❌ Please enter a prompt"
+
+    try:
+        encoded = text_token_ids(prompt, tokenizer).to(device)
+
+        with torch.no_grad():
+            token_ids = generate_text_better(
+                model=model,
+                idx=encoded,
+                max_new_tokens=int(max_tokens),
+                context_size=checkpoint['config']['context_length'],
+                temperature=float(temperature),
+                top_k=int(top_k)
+            )
+
+        generated_text = token_text_ids(token_ids, tokenizer)
+
+        output = f"""🤖 GENERATED TEXT:
+{generated_text}
+
+⚙️ PARAMETERS:
+• Tokens: {max_tokens}
+• Temperature: {temperature}
+• Top-K: {top_k}
+• Device: {device}
+"""
+        return output
 
-    **How to use:**
-    1. Paste your court document in the text box
-    2. Adjust the summary length parameters (optional)
-    3. Click "Submit" to generate summary
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
 
-    **Note:** First run may take longer as the model loads.
-    """,
 
-    # Example inputs for users to try
+generate_interface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="💭 Prompt", lines=5, placeholder="Enter your prompt..."),
+        gr.Slider(10, 500, value=100, step=10, label="Max Tokens"),
+        gr.Slider(0.1, 2.0, value=0.8, step=0.1, label="Temperature"),
+        gr.Slider(1, 100, value=50, step=1, label="Top-K")
+    ],
+    outputs=gr.Textbox(label="✨ Generated Text", lines=15),
+    title="🚀 Text Generator",
+    description="Generate text using custom GPT model",
     examples=[
-        [
-            "Putusan Pengadilan Negeri Jakarta ini memutuskan bahwa terdakwa terbukti bersalah melakukan tindak pidana korupsi dengan merugikan negara sebesar 5 miliar rupiah. Majelis hakim mempertimbangkan bahwa terdakwa telah dengan sengaja memperkaya diri sendiri dan menyalahgunakan wewenang sebagai pejabat publik. Berdasarkan pertimbangan tersebut, terdakwa dijatuhi hukuman penjara selama 8 tahun dan denda 500 juta rupiah.",
-            200,
-            30,
-            4
-        ]
+        ["Once upon a time,", 150, 0.8, 50],
+        ["The future of AI is", 100, 0.7, 40]
     ]
 )
 
 # ============================================================================
-# Step 4: Launch the app
+# TAB 3: About / Info
 # ============================================================================
-if __name__ == "__main__":
-    # For local testing:
-    # demo.launch(share=False)
 
-    # For Hugging Face Spaces deployment:
-    # Note: In Gradio 6.0+, theme is passed to launch() not Interface()
+def get_system_info():
+    """Display system and model information"""
+
+    info = f"""# 🤖 Indonesian AI Tools
+
+## System Information
+- **Device**: {device}
+- **PyTorch Version**: {torch.__version__}
+- **CUDA Available**: {torch.cuda.is_available()}
+
+## Available Models
+- **✅ Summarization**: {'Loaded' if summarizer_available else '❌ Not Available'}
+- **Text Generation**: {'✅ Loaded' if gpt_available else '❌ Not Available'}
+
+## Features
+1. **Court Document Summarization**
+   - Summarizes Indonesian legal documents
+   - Uses pre-trained transformer model
+   - Adjustable summary length
+
+2. **Text Generation** (if available)
+   - Custom GPT model
+   - Trained on specific corpus
+   - Creative text generation
+
+## Usage Tips
+- For summarization: Use 4-6 beams for best quality
+- For generation: Temperature 0.7-0.9 for creative output
+- Adjust parameters based on your needs
+
+## Technical Details
+- Framework: Gradio + PyTorch
+- Deployment: Hugging Face Spaces compatible
+- GPU Support: Automatic detection
+"""
+    return info
+
+
+info_interface = gr.Interface(
+    fn=get_system_info,
+    inputs=[],
+    outputs=gr.Markdown(),
+    title="ℹ️ About",
+    description="System information and usage guide"
+)
+
+# ============================================================================
+# Create Combined Tabbed Interface
+# ============================================================================
+# This is the KEY difference from Flask:
+# - One file combines all features
+# - Tabs organize different functions
+# - No routing needed - Gradio handles everything
+
+demo = gr.TabbedInterface(
+    # List of all interfaces (tabs)
+    interface_list=[
+        summarize_interface,
+        generate_interface if gpt_available else info_interface,
+        info_interface
+    ],
+
+    # Tab names
+    tab_names=[
+        "📄 Summarize",
+        "🚀 Generate" if gpt_available else "ℹ️ Info",
+        "ℹ️ About"
+    ],
+
+    # Overall title
+    title="🇮🇩 Indonesian AI Tools"
+)
+
+# ============================================================================
+# Launch Application
+# ============================================================================
+if __name__ == "__main__":
+    # For Hugging Face Spaces:
     demo.launch(
-        server_name="0.0.0.0",  # Allow external access
-        server_port=7860,        # Default HF Spaces port
-        share=False              # Don't create public link (HF does this)
+        server_name="0.0.0.0",  # Allow external connections
+        server_port=7860,        # HF Spaces default port
+        share=False              # HF handles sharing
     )
+
+    # For local testing with public URL:
+    # demo.launch(share=True)
+
+    # For local testing only:
+    # demo.launch()