Update app.py

app.py

@@ -15,16 +15,14 @@ print(f"✓ Tokenizer vocab size: {len(tokenizer)}")
 print(f"✓ Model parameters: {model.num_parameters()}")
 
 def smart_fallback_optimize(prompt):
-    """Fallback optimization using heuristics when model fails"""
-    # Remove common filler words and phrases
+    """Fallback optimization when model fails"""
     fillers = {
         'please', 'could', 'would', 'can', 'you', 'the', 'a', 'an',
         'very', 'really', 'quite', 'just', 'actually', 'basically',
         'literally', 'honestly', 'i think', 'in my opinion',
-        'it seems', 'kind of', 'sort of'
+        'it seems', 'kind of', 'sort of', 'i want', 'i would like'
     }
     
-    # Common verbose phrases to simplify
     replacements = {
         r'\bcan you please\b': '',
         r'\bcould you please\b': '',
@@ -33,32 +31,23 @@ def smart_fallback_optimize(prompt):
         r'\bi want to\b': '',
         r'\bhelp me\b': '',
         r'\bfor me\b': '',
-        r'\bthat is\b': '',
-        r'\bwhich is\b': '',
     }
     
     optimized = prompt.lower()
     
-    # Apply replacements
     for pattern, replacement in replacements.items():
         optimized = re.sub(pattern, replacement, optimized)
     
-    # Remove filler words
     words = optimized.split()
     words = [w for w in words if w not in fillers]
-    
-    # Join and clean up
     optimized = ' '.join(words).strip()
     
-    # Capitalize first letter
     if optimized:
         optimized = optimized[0].upper() + optimized[1:]
     
-    # If too short or empty, return a simplified version
     if not optimized or len(optimized) < 5:
-        # Just take key words
         words = prompt.split()
-        optimized = ' '.join(words[:5])  # First 5 words
+        optimized = ' '.join(words[:8])
     
     return optimized
 
@@ -70,15 +59,9 @@ def optimize_prompt(prompt, preserve_meaning=True):
     print(f"\n=== OPTIMIZING ===")
     print(f"Input: {prompt[:100]}")
     
-    # Try model first
     try:
         input_text = "optimize: " + prompt
-        input_ids = tokenizer.encode(
-            input_text, 
-            return_tensors="pt", 
-            truncation=True, 
-            max_length=512
-        )
+        input_ids = tokenizer.encode(input_text, return_tensors="pt", truncation=True, max_length=512)
         
         with torch.no_grad():
             outputs = model.generate(
@@ -94,34 +77,27 @@ def optimize_prompt(prompt, preserve_meaning=True):
         
         optimized = tokenizer.decode(outputs[0], skip_special_tokens=True)
         
-        # Calculate how much it actually optimized
         original_tokens = len(tokenizer.encode(prompt))
         optimized_tokens = len(tokenizer.encode(optimized))
         reduction_rate = (original_tokens - optimized_tokens) / original_tokens
         
-        # If model barely optimized (less than 10% reduction), use fallback
         if reduction_rate < 0.1 or not optimized or len(optimized.strip()) < 3:
-            print("⚠ Model optimization weak, using smart fallback")
+            print("⚠ Model weak, using fallback")
             optimized = smart_fallback_optimize(prompt)
         else:
-            print(f"✓ Model optimization successful")
+            print(f"✓ Model success")
         
     except Exception as e:
-        print(f"⚠ Model error: {e}, using fallback")
+        print(f"⚠ Error: {e}, using fallback")
         optimized = smart_fallback_optimize(prompt)
     
     print(f"Output: {optimized}")
     
-    # Calculate final metrics
     original_tokens = len(tokenizer.encode(prompt))
     optimized_tokens = len(tokenizer.encode(optimized))
     tokens_saved = max(0, original_tokens - optimized_tokens)
     reduction = (tokens_saved / max(original_tokens, 1)) * 100
-    
-    # Energy: 0.000002 Wh per token
     energy = tokens_saved * 0.000002
-    
-    # CO2: 475g per kWh
     co2 = energy * 475 / 1000
     
     print(f"Tokens: {original_tokens} → {optimized_tokens} (saved {tokens_saved})")
@@ -136,151 +112,9 @@ def optimize_prompt(prompt, preserve_meaning=True):
         f"{co2:.6f} g"
     )
 
-# CSS matching your website style
-custom_css = """
-/* Main container - darker background like website */
-.gradio-container {
-    background: linear-gradient(135deg, #1a2332 0%, #0d1520 100%) !important;
-    padding: 2rem !important;
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
-}
-
-/* Primary button - gradient like website */
-.gr-button-primary {
-    background: linear-gradient(135deg, #34d399, #3b82f6) !important;
-    border: none !important;
-    border-radius: 10px !important;
-    font-weight: 600 !important;
-    padding: 14px 28px !important;
-    font-size: 1.1rem !important;
-    transition: all 0.3s ease !important;
-    box-shadow: 0 4px 12px rgba(52, 211, 153, 0.3) !important;
-}
-
-.gr-button-primary:hover {
-    transform: translateY(-3px) !important;
-    box-shadow: 0 6px 20px rgba(52, 211, 153, 0.5) !important;
-}
-
-/* Textareas - match website input boxes */
-textarea {
-    background: rgba(20, 30, 45, 0.95) !important;
-    border: 2px solid rgba(52, 211, 153, 0.3) !important;
-    color: #e2e8f0 !important;
-    border-radius: 10px !important;
-    padding: 1rem !important;
-    font-size: 1rem !important;
-    line-height: 1.6 !important;
-}
-
-textarea:focus {
-    border-color: #34d399 !important;
-    box-shadow: 0 0 0 3px rgba(52, 211, 153, 0.15) !important;
-    outline: none !important;
-}
-
-/* Labels - green accent */
-label {
-    color: #34d399 !important;
-    font-weight: 600 !important;
-    font-size: 1.1rem !important;
-    margin-bottom: 0.5rem !important;
-}
-
-/* Title styling */
-h1 {
-    background: linear-gradient(135deg, #34d399, #3b82f6);
-    -webkit-background-clip: text !important;
-    -webkit-text-fill-color: transparent !important;
-    font-size: 2.8rem !important;
-    text-align: center !important;
-    font-weight: 700 !important;
-    margin-bottom: 0.5rem !important;
-}
-
-/* Subtitle */
-.gradio-container h3 {
-    color: #94a3b8 !important;
-    text-align: center !important;
-    font-weight: 400 !important;
-    font-size: 1.2rem !important;
-    margin-bottom: 2rem !important;
-}
-
-/* Result boxes - colorful like website */
-.gr-box {
-    background: rgba(20, 30, 45, 0.8) !important;
-    border: 2px solid rgba(52, 211, 153, 0.3) !important;
-    border-radius: 10px !important;
-    padding: 0.75rem !important;
-}
-
-/* Specific result box colors */
-div[class*="token_info"] .gr-box,
-div:nth-child(1) .gr-box {
-    border-color: #34d399 !important;
-    background: linear-gradient(135deg, rgba(52, 211, 153, 0.15), rgba(59, 130, 246, 0.15)) !important;
-}
-
-div[class*="tokens_saved"] .gr-box,
-div:nth-child(2) .gr-box {
-    border-color: #fbbf24 !important;
-    background: linear-gradient(135deg, rgba(251, 191, 36, 0.15), rgba(245, 158, 11, 0.15)) !important;
-}
-
-div[class*="reduction"] .gr-box,
-div:nth-child(3) .gr-box {
-    border-color: #8b5cf6 !important;
-    background: linear-gradient(135deg, rgba(139, 92, 246, 0.15), rgba(124, 58, 237, 0.15)) !important;
-}
-
-div[class*="energy"] .gr-box,
-div:nth-child(4) .gr-box {
-    border-color: #22d3ee !important;
-    background: linear-gradient(135deg, rgba(34, 211, 238, 0.15), rgba(6, 182, 212, 0.15)) !important;
-}
-
-div[class*="co2"] .gr-box,
-div:nth-child(5) .gr-box {
-    border-color: #10b981 !important;
-    background: linear-gradient(135deg, rgba(16, 185, 129, 0.15), rgba(52, 211, 153, 0.15)) !important;
-}
-
-/* Checkbox */
-.gr-checkbox {
-    color: #cbd5e1 !important;
-}
-
-/* Info text */
-.gr-form span {
-    color: #94a3b8 !important;
-}
-
-/* Remove footer */
-.footer {
-    display: none !important;
-}
-
-/* Section headers */
-.gr-markdown h3 {
-    color: #34d399 !important;
-    font-size: 1.5rem !important;
-    margin-top: 1.5rem !important;
-    margin-bottom: 1rem !important;
-}
-
-/* Bottom text */
-.gr-markdown p {
-    color: #94a3b8 !important;
-    text-align: center !important;
-}
-
-/* Horizontal rule */
-.gr-markdown hr {
-    border-color: rgba(52, 211, 153, 0.2) !important;
-    margin: 1.5rem 0 !important;
-}
-"""
+# Load custom CSS
+with open("custom.css", "r") as f:
+    custom_css = f.read()
 
 # Create interface
 with gr.Blocks(css=custom_css) as demo:
@@ -319,7 +153,6 @@ with gr.Blocks(css=custom_css) as demo:
     
     co2 = gr.Textbox(label="🌍 CO₂ Reduced", interactive=False)
     
-    # Connect button
     btn.click(
         optimize_prompt,
         inputs=[prompt_box, preserve_box],
@@ -329,6 +162,5 @@ with gr.Blocks(css=custom_css) as demo:
     gr.Markdown("---")
     gr.Markdown("✅ **Built with 💚 for a greener AI future | Powered by T5**")
 
-# Launch
 if __name__ == "__main__":
     demo.launch()