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app.py

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+"""
+Netra AI - Construction Material Classifier
+MVP Demo using Gemini Vision API
+"""
+
+import gradio as gr
+import google.generativeai as genai
+import os
+from PIL import Image
+import base64
+from io import BytesIO
+
+# Material classification prompt
+CLASSIFICATION_PROMPT = """
+You are a construction material classifier for Netra AI. Classify this image into ONE of these 4 classes:
+
+1. **Reet (Sand)**: All grades of sand - from fine powdery sand to coarse gritty sand. Shows smooth to granular texture.
+
+2. **12mm VSI**: Uniform, small, cubical "clean" stones. Very consistent size (~10-15mm). Manufactured aggregate with sharp edges.
+
+3. **Stone**: Large, irregular boulders. Significant size variation, deep voids between rocks. Raw, unprocessed appearance.
+
+4. **GSB (Graded Stone Base)**: Mixed-size material with rocks, gravel, grit AND fine dust/sand filling gaps. Key indicator is fine dust matrix.
+
+Respond in this exact format:
+CLASS: [class name]
+CONFIDENCE: [High/Medium/Low]
+REASONING: [One sentence explanation of visual features that led to this classification]
+"""
+
+
+def classify_material(image):
+    """Classify construction material from image using Netra AI"""
+
+    if image is None:
+        return "Please upload an image", "", ""
+
+    # Get API key from environment
+    api_key = os.getenv("GOOGLE_API_KEY", "")
+    if not api_key:
+        return "❌ System Error", "", "AI service not configured. Please contact support."
+
+    try:
+        # Configure Gemini
+        genai.configure(api_key=api_key)
+        model = genai.GenerativeModel('gemini-2.5-flash')
+
+        # Convert to PIL Image if needed
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+
+        # Generate classification
+        response = model.generate_content([CLASSIFICATION_PROMPT, image])
+        result_text = response.text.strip()
+
+        # Parse response
+        lines = result_text.split('\n')
+        class_name = ""
+        confidence = ""
+        reasoning = ""
+
+        for line in lines:
+            if line.startswith("CLASS:"):
+                class_name = line.replace("CLASS:", "").strip()
+            elif line.startswith("CONFIDENCE:"):
+                confidence = line.replace("CONFIDENCE:", "").strip()
+            elif line.startswith("REASONING:"):
+                reasoning = line.replace("REASONING:", "").strip()
+
+        # Format output
+        if class_name:
+            result = f"✅ RESULT:\n\n{class_name}"
+        else:
+            result = "⏳ AWAITING CLASSIFICATION"
+
+        confidence_display = f"Confidence: {confidence}" if confidence else ""
+        reasoning_display = f"💡 **Analysis:** {reasoning}" if reasoning else result_text
+
+        return result, confidence_display, reasoning_display
+
+    except Exception as e:
+        error_msg = str(e)
+        if "API_KEY_INVALID" in error_msg or "401" in error_msg:
+            return "❌ Invalid API Key", "", "Please check your Gemini API key"
+        elif "quota" in error_msg.lower():
+            return "❌ API Quota Exceeded", "", "Your API key has exceeded its quota"
+        else:
+            return f"❌ Error: {error_msg[:100]}", "", "Please try again"
+
+
+# Custom CSS
+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:contact@netraai.com)
+
+                </div>
+                """
+            )
+
+    # Example images
+    gr.Markdown("### 📷 Try These Sample Images")
+    gr.Examples(
+        examples=[
+            ["sanity_test_dataset/12mm_VSI_01.jpeg"],
+            ["sanity_test_dataset/Reet_01_fine.jpeg"],
+            ["sanity_test_dataset/Stone_01.jpeg"],
+            ["sanity_test_dataset/GSB_01.jpeg"],
+        ],
+        inputs=image_input,
+        label="Sample Materials"
+    )
+
+    # Connect button
+    classify_btn.click(
+        fn=classify_material,
+        inputs=[image_input],
+        outputs=[result_output, confidence_output, reasoning_output]
+    )
+
+# Launch
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )