Add proxy app that calls protected backend

app.py

@@ -0,0 +1,161 @@
+"""
+Netra AI - Public Interface
+Calls protected backend space for classification
+"""
+
+import gradio as gr
+from gradio_client import Client
+from huggingface_hub import login
+import os
+
+# Authenticate and connect to protected backend
+HF_TOKEN = os.getenv("HF_TOKEN")
+if HF_TOKEN:
+    login(token=HF_TOKEN)
+
+backend = Client("rg321/Construction_Material_Classification")
+
+
+def classify_material(image):
+    """Proxy function that calls protected backend"""
+    if image is None:
+        return "Please upload an image", "", ""
+
+    try:
+        # Call protected space API
+        result = backend.predict(image=image, api_name="/classify_material")
+
+        # result is tuple: (classification, confidence, reasoning)
+        return result[0], result[1], result[2]
+
+    except Exception as e:
+        error_msg = str(e)
+        return f"❌ Classification Error", "", f"Please try again or contact support. Error: {error_msg[:100]}"
+
+
+# Custom CSS (same as original)
+custom_css = """
+#header {
+    text-align: center;
+    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+    padding: 20px;
+    border-radius: 10px;
+    color: white;
+}
+#result {
+    font-size: 72px;
+    font-weight: 900;
+    text-align: center;
+    padding: 50px 30px;
+    border-radius: 20px;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    margin-bottom: 25px;
+    box-shadow: 0 8px 16px rgba(0,0,0,0.2);
+    text-transform: uppercase;
+    letter-spacing: 2px;
+    line-height: 1.2;
+}
+#confidence {
+    font-size: 24px;
+    font-weight: 600;
+    text-align: center;
+    padding: 15px;
+    background: #f0f9ff;
+    border-radius: 10px;
+    margin-bottom: 20px;
+}
+#reasoning {
+    font-size: 14px;
+    text-align: left;
+    padding: 15px;
+    background: #f9fafb;
+    border-radius: 8px;
+    margin-bottom: 20px;
+    color: #4b5563;
+}
+#about {
+    font-size: 13px;
+    color: #6b7280;
+    padding-top: 20px;
+    border-top: 1px solid #e5e7eb;
+}
+"""
+
+# Build Gradio interface
+with gr.Blocks(css=custom_css, title="Netra AI - Material Classifier") as demo:
+
+    gr.Markdown(
+        """
+        <div id="header">
+        <h1>🏗️ Netra AI - Construction Material Classifier</h1>
+        <p>AI-powered material identification for construction sites</p>
+        </div>
+        """,
+        elem_id="header"
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📸 Upload Material Image")
+            image_input = gr.Image(
+                type="pil",
+                label="Construction Material Photo",
+                height=400
+            )
+
+            classify_btn = gr.Button("🚀 Classify Material", variant="primary", size="lg")
+
+            gr.Markdown(
+                """
+                **Supported Materials:**
+                - 🏖️ Reet (Sand)
+                - 🔷 12mm VSI (Uniform Aggregate)
+                - 🪨 Stone (Large Boulders)
+                - 🏗️ GSB (Mixed Graded Base)
+                """
+            )
+
+        with gr.Column(scale=1):
+            result_output = gr.Markdown("", elem_id="result")
+            confidence_output = gr.Markdown("", elem_id="confidence")
+            reasoning_output = gr.Markdown("", elem_id="reasoning")
+
+            gr.Markdown(
+                """
+                <div id="about">
+
+                ### 📊 About This Demo
+
+                This is a proof-of-concept for **Netra AI's** automated material classification system.
+
+                **Technology:**
+                - Vision AI for real-time material identification
+                - Prevents grade fraud at construction sites
+                - Automates dispatch logging
+
+                **Use Cases:**
+                - Gate monitoring at crusher plants
+                - Dispatch verification
+                - Quality control
+
+                ---
+                **Netra AI** - *Making every tonne count.*
+
+                For commercial inquiries: [Contact Us](mailto:guptaraghu321@gmail.com)
+
+                </div>
+                """
+            )
+
+
+    # Connect button
+    classify_btn.click(
+        fn=classify_material,
+        inputs=[image_input],
+        outputs=[result_output, confidence_output, reasoning_output]
+    )
+
+# Launch
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio==5.9.1
+gradio_client
+huggingface_hub