Update app.py

app.py

@@ -1,59 +1,56 @@
-import os
 import gradio as gr
-from together import Together
-import base64
+import random
 
-# Initialize the Together client
-client = Together(api_key=os.environ.get('TOGETHER_API_KEY'))
+def generate_app(image):
+    # Placeholder function for app generation
+    # In a real scenario, this would involve image analysis and code generation
+    components = ["Header", "Sidebar", "MainContent", "Footer"]
+    code = f"""
+import React from 'react';
+import {{ {', '.join(components)} }} from './components';
 
-def process_image(image):
-    # Convert the image to base64
-    buffered = BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode()
-    
-    # Prepare the messages for the API call
-    messages = [
-        {"role": "system", "content": "You are an AI assistant that can analyze images and generate code based on their content."},
-        {"role": "user", "content": [
-            {"type": "image_url", "image_url": f"data:image/png;base64,{img_str}"},
-            {"type": "text", "text": "Analyze this image and generate Python code that could recreate or represent the main elements seen in the image."}
-        ]}
-    ]
-    
-    # Make the API call
-    response = client.chat.completions.create(
-        model="meta-llama/Llama-Vision-Free",
-        messages=messages,
-        max_tokens=512,
-        temperature=0.7,
-        top_p=0.7,
-        top_k=50,
-        repetition_penalty=1,
-        stop=["<|eot_id|>", "<|eom_id|>"]
-    )
+function App() {{
+  return (
+    <div className="app">
+      {' '.join(f'<{component} />' for component in components)}
+    </div>
+  );
+}}
+
+export default App;
+    """
+    return code
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Turn your wireframe into an app")
+    gr.Markdown("Upload an image of your website design and we'll build it for you with React + Tailwind.")
     
-    # Extract the generated code from the response
-    generated_code = response.choices[0].message.content
+    with gr.Row():
+        with gr.Column(scale=2):
+            with gr.Row():
+                napkin_img = gr.Image(value="path/to/napkin_sketch.png", label="Napkin")
+                react_img = gr.Image(value="path/to/react_app.png", label="React app")
+            
+            file_output = gr.File(label="Download React App")
+        
+        with gr.Column():
+            image_input = gr.Image(label="Upload a screenshot", elem_id="image_upload")
+            example_link = gr.Markdown("Need an example image? [Try ours](#).")
+            
+            model_dropdown = gr.Dropdown(
+                choices=["Llama 3.2 90B Vision", "Other models..."],
+                value="Llama 3.2 90B Vision",
+                label="AI Model"
+            )
+            
+            generate_button = gr.Button("Generate app", variant="primary")
     
-    # Generate HTML to display the code with syntax highlighting
-    html_output = f"""
-    <pre><code class="language-python">{generated_code}</code></pre>
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/styles/default.min.css">
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.7.0/highlight.min.js"></script>
-    <script>hljs.highlightAll();</script>
-    """
+    code_output = gr.Code(language="javascript", label="Generated React Code")
     
-    return html_output
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=process_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.HTML(),
-    title="Llama Vision Free Code Generation",
-    description="Upload an image, and this demo will use the Llama Vision Free model to analyze it and generate relevant Python code."
-)
+    generate_button.click(
+        fn=generate_app,
+        inputs=[image_input],
+        outputs=[code_output, file_output]
+    )
 
-# Launch the interface
-iface.launch()
+demo.launch()