Update app.py

app.py

@@ -1,28 +1,13 @@
 # app.py
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import requests
 import gradio as gr
 import torch
 
-# Check if CUDA is available
-if torch.cuda.is_available():
-    # Configure 8-bit quantization
-    quantization_config = BitsAndBytesConfig(
-        load_in_8bit=True,
-        llm_int8_threshold=6.0
-    )
-else:
-    # Skip quantization if CUDA is not available
-    quantization_config = None
-
-# Load the Hugging Face model and tokenizer
-model_name = "gpt2"  # Smaller and faster model
+# Load the Hugging Face model and tokenizer (only once)
+model_name = "distilgpt2"  # Smaller and faster model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    quantization_config=quantization_config,
-    device_map="auto" if torch.cuda.is_available() else None
-)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
 # Groq API configuration
 GROQ_API_KEY = "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"
@@ -48,7 +33,7 @@ def generate_smart_contract(language, requirements):
     
     # Use the Hugging Face model to generate code
     inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
-    outputs = model.generate(**inputs, max_length=300)  # Reduced max_length
+    outputs = model.generate(**inputs, max_length=150)  # Reduced max_length
     generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
     
     # Enhance the code using Groq API
@@ -56,16 +41,107 @@ def generate_smart_contract(language, requirements):
     
     return enhanced_code
 
+# Custom CSS for a 3D CGI Figma-like feel
+custom_css = """
+body {
+    font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;
+    background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%);
+    color: #fff;
+    perspective: 1000px;
+    overflow: hidden;
+}
+
+.gradio-container {
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 15px;
+    padding: 20px;
+    box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);
+    backdrop-filter: blur(10px);
+    border: 1px solid rgba(255, 255, 255, 0.3);
+    transform-style: preserve-3d;
+    transform: rotateY(0deg) rotateX(0deg);
+    transition: transform 0.5s ease;
+}
+
+.gradio-container:hover {
+    transform: rotateY(10deg) rotateX(10deg);
+}
+
+.gradio-input, .gradio-output {
+    background: rgba(255, 255, 255, 0.2);
+    border: none;
+    border-radius: 10px;
+    padding: 10px;
+    color: #fff;
+    transform-style: preserve-3d;
+    transition: transform 0.3s ease;
+}
+
+.gradio-input:focus, .gradio-output:focus {
+    background: rgba(255, 255, 255, 0.3);
+    outline: none;
+    transform: translateZ(20px);
+}
+
+.gradio-button {
+    background: linear-gradient(135deg, #6a11cb 0%, #2575fc 100%);
+    border: none;
+    border-radius: 10px;
+    color: #fff;
+    padding: 10px 20px;
+    font-size: 16px;
+    cursor: pointer;
+    transition: background 0.3s ease, transform 0.3s ease;
+    transform-style: preserve-3d;
+}
+
+.gradio-button:hover {
+    background: linear-gradient(135deg, #2575fc 0%, #6a11cb 100%);
+    transform: translateZ(10px);
+}
+
+h1 {
+    text-align: center;
+    font-size: 2.5em;
+    margin-bottom: 20px;
+    background: linear-gradient(135deg, #6a11cb 0%, #2575fc 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    transform-style: preserve-3d;
+    transform: translateZ(30px);
+}
+
+@keyframes float {
+    0% {
+        transform: translateY(0) translateZ(0);
+    }
+    50% {
+        transform: translateY(-10px) translateZ(10px);
+    }
+    100% {
+        transform: translateY(0) translateZ(0);
+    }
+}
+
+.gradio-container {
+    animation: float 4s ease-in-out infinite;
+}
+"""
+
 # Gradio interface for the app
 def generate_contract(language, requirements):
     return generate_smart_contract(language, requirements)
 
 interface = gr.Interface(
     fn=generate_contract,
-    inputs=["text", "text"],
-    outputs="text",
+    inputs=[
+        gr.Textbox(label="Programming Language", placeholder="e.g., Solidity"),
+        gr.Textbox(label="Requirements", placeholder="e.g., ERC20 token with minting functionality")
+    ],
+    outputs=gr.Textbox(label="Generated Smart Contract"),
     title="Smart Contract Generator",
-    description="Generate smart contracts using AI."
+    description="Generate smart contracts using AI.",
+    css=custom_css
 )
 
 # Launch the Gradio app