Update app.py

app.py

@@ -381,22 +381,22 @@ class FoduucomStyleBabyCryClassifier:
 # Initialize classifier
 classifier = FoduucomStyleBabyCryClassifier()
 
-def predict_api(audio_url):
+def predict_baby_cry(audio_url):
     """
-    Main API function - this will be the primary API endpoint
+    Main prediction function for API and UI
     """
-    print(f"🔍 API Call received: {audio_url}")
+    print(f"🔍 Prediction request: {audio_url}")
     
-    if not audio_url:
+    if not audio_url or audio_url.strip() == "":
         return {"success": False, "error": "No audio URL provided"}
     
     result = classifier.predict(audio_url)
-    print(f"✅ API Result: {result.get('prediction', 'error')}")
+    print(f"✅ Prediction result: {result.get('prediction', 'error')}")
     
     return result
 
 def web_interface_predict(audio_file):
-    """Web interface function"""
+    """Web interface function for file uploads"""
     if audio_file is None:
         return "❌ No audio file provided", "{}"
     
@@ -441,96 +441,112 @@ def web_interface_predict(audio_file):
     
     return summary, json.dumps(result, indent=2)
 
-# Create the main Gradio Blocks interface
-with gr.Blocks(title="🍼 Baby Cry Classifier - HF API Ready", theme=gr.themes.Soft()) as demo:
-    
+# Create a simple Interface that will work with HuggingFace API
+api_interface = gr.Interface(
+    fn=predict_baby_cry,
+    inputs=gr.Textbox(
+        label="Audio URL",
+        placeholder="https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3",
+        info="Enter the URL of an audio file to analyze"
+    ),
+    outputs=gr.JSON(label="Baby Cry Analysis"),
+    title="🍼 Baby Cry Classifier - API Ready",
+    description="Analyze baby cries to understand what your baby needs. This interface works with both UI and API calls.",
+    examples=[
+        ["https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3"]
+    ]
+)
+
+# Create a file upload interface
+upload_interface = gr.Interface(
+    fn=web_interface_predict,
+    inputs=gr.Audio(label="Upload Baby Cry Audio", type="filepath"),
+    outputs=[
+        gr.Markdown(label="Analysis Summary"),
+        gr.Code(label="JSON Data", language="json")
+    ],
+    title="🍼 Baby Cry Classifier - File Upload",
+    description="Upload an audio file directly to analyze baby cries."
+)
+
+# Combine interfaces
+demo = gr.TabbedInterface(
+    [api_interface, upload_interface],
+    ["🌐 API Interface", "📁 File Upload"],
+    title="🍼 Baby Cry Classifier"
+)
+
+# Add documentation as a separate Blocks interface
+with gr.Blocks() as full_demo:
     gr.HTML("""
     <div style="text-align: center; margin-bottom: 20px;">
         <h1>🍼 Baby Cry Classifier</h1>
-        <p><em>✅ FIXED: Now Works with HuggingFace API!</em></p>
+        <p><em>✅ API Ready - Use Python Client for Best Results!</em></p>
     </div>
     """)
     
-    with gr.Tab("🌐 API Interface"):
-        gr.Markdown("### Use this interface for API calls")
-        
-        with gr.Row():
-            with gr.Column():
-                api_url_input = gr.Textbox(
-                    label="Audio URL", 
-                    placeholder="https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3",
-                    info="Enter the URL of an audio file to analyze"
-                )
-                api_submit_btn = gr.Button("🔍 Analyze Audio", variant="primary")
-            
-            with gr.Column():
-                api_output = gr.JSON(label="Analysis Result")
-        
-        # This creates the API endpoint
-        api_submit_btn.click(
-            fn=predict_api,
-            inputs=[api_url_input],
-            outputs=[api_output],
-            api_name="predict"  # This creates the /predict endpoint
-        )
-        
-        gr.Examples(
-            examples=[["https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3"]],
-            inputs=[api_url_input]
-        )
+    # Render the main demo
+    demo.render()
     
-    with gr.Tab("📁 File Upload"):
-        gr.Markdown("### Upload audio files directly")
-        
-        with gr.Row():
-            with gr.Column():
-                file_input = gr.Audio(label="Upload Baby Cry Audio", type="filepath")
-                file_submit_btn = gr.Button("🔍 Analyze Upload", variant="primary")
-            
-            with gr.Column():
-                file_summary_output = gr.Markdown(label="Analysis Summary")
-                file_json_output = gr.Code(label="JSON Data", language="json")
-        
-        file_submit_btn.click(
-            fn=web_interface_predict,
-            inputs=[file_input],
-            outputs=[file_summary_output, file_json_output]
-        )
-    
-    with gr.Tab("📖 API Documentation"):
+    # Add documentation
+    with gr.Accordion("📖 API Documentation & Usage", open=True):
         gr.Markdown("""
-        ## 🔗 HuggingFace API Usage
+        ## 🚨 Important: HuggingFace API Limitations
         
-        **✅ WORKING Curl Command:**
-        ```bash
-        curl -X POST \
-          -H "Content-Type: application/json" \
-          --data '{"data": ["https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3"]}' \
-          https://jitender1278-babycry.hf.space/call/predict
-        ```
+        **HuggingFace Spaces no longer supports direct curl commands** due to their queue system. 
+        Here are the working alternatives:
         
-        **Alternative with session_hash:**
-        ```bash
-        curl -X POST \
-          -H "Content-Type: application/json" \
-          --data '{"data": ["https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3"], "session_hash": "demo123"}' \
-          https://jitender1278-babycry.hf.space/run/predict
-        ```
+        ## ✅ Method 1: Python Client (RECOMMENDED)
         
-        **Python Client (Recommended):**
         ```python
         from gradio_client import Client
         
-        client = Client("https://jitender1278-babycry.hf.space")
+        # Initialize client
+        client = Client("https://jitender1278-babycry.hf.space/")
+        
+        # Make prediction
         result = client.predict(
             "https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3",
             api_name="/predict"
         )
+        
         print(result)
         ```
         
+        ## ✅ Method 2: JavaScript/Node.js
+        
+        ```javascript
+        import { Client } from "@gradio/client";
+        
+        const client = await Client.connect("https://jitender1278-babycry.hf.space/");
+        const result = await client.predict("/predict", {
+            audio_url: "https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3"
+        });
+        
+        console.log(result.data);
+        ```
+        
+        ## ✅ Method 3: Python Requests (Alternative)
+        
+        ```python
+        import requests
+        import json
+        
+        # This uses the gradio_client internally
+        from gradio_client import Client
+        
+        def analyze_baby_cry(audio_url):
+            client = Client("https://jitender1278-babycry.hf.space/")
+            result = client.predict(audio_url, api_name="/predict")
+            return result
+        
+        # Usage
+        audio_url = "https://raw.githubusercontent.com/jiten-kmar/python-projects/main/baby-crying-32232.mp3"
+        analysis = analyze_baby_cry(audio_url)
+        print(json.dumps(analysis, indent=2))
+        ```
+        
         ## 📝 Response Format
-        The API returns a JSON object with the following structure:
         
         ```json
         {
@@ -548,23 +564,35 @@ with gr.Blocks(title="🍼 Baby Cry Classifier - HF API Ready", theme=gr.themes.
             "immediate": "Offer feeding - check if it's been 2-3 hours since last meal",
             "details": "Look for additional hunger cues: rooting reflex, sucking motions, bringing hands to mouth"
           },
-          "timestamp": "2025-06-12T12:00:00",
-          "session_id": "abc123",
+          "timestamp": "2025-06-12T12:00:00.000000",
+          "session_id": "abc12345",
           "model_info": "foduucom-style implementation"
         }
         ```
         
-        ## 🚨 Important Notes
-        - Use `/call/predict` for the primary API endpoint
-        - The API accepts audio file URLs (not file uploads via curl)
-        - Supported audio formats: MP3, WAV, M4A, FLAC
+        ## 🔧 Installation
+        
+        ```bash
+        pip install gradio-client
+        ```
+        
+        ## 📋 Supported Audio Formats
+        - MP3, WAV, M4A, FLAC, OGG
         - Maximum file size: ~10MB
-        - Response time: 5-15 seconds depending on audio length
+        - Audio URLs must be publicly accessible
+        
+        ## ⚠️ Why Curl Doesn't Work
+        HuggingFace Spaces now uses a queue system that requires WebSocket connections for real-time processing. 
+        Direct HTTP POST requests are blocked to prevent abuse and ensure fair resource allocation.
+        
+        Use the Python client above for the best API experience!
         """)
 
 if __name__ == "__main__":
-    print("🚀 Starting Baby Cry Classifier with HuggingFace API support...")
-    demo.launch(
+    print("🚀 Starting Baby Cry Classifier...")
+    print("📝 Note: Use Python gradio_client for API access (curl not supported)")
+    
+    full_demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
         show_error=True