Update app.py

app.py

@@ -1,124 +1,49 @@
-import gradio as gr
-from transformers import pipeline
-import numpy as np
-from PIL import Image
-import io
-import base64
+import json
+import os
+import logging
+from datetime import datetime
 
-# Initialize the sentiment analysis pipeline (using a lightweight model for CPU)
-sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.FileHandler("/home/pi5/horrorvidmaker/test_write_json.log"),
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger(__name__)
 
-# Mock text-to-image function (simulates image generation for CPU compatibility)
-def generate_mock_image(text_prompt, width=200, height=200):
-    # Create a simple gradient image as a placeholder (to avoid heavy dependencies)
-    img_array = np.zeros((height, width, 3), dtype=np.uint8)
-    for i in range(height):
-        for j in range(width):
-            img_array[i, j] = [(i % 255), (j % 255), ((i + j) % 255)]  # RGB gradient
-    img = Image.fromarray(img_array)
-    
-    # Save image to a buffer for Gradio
-    buffered = io.BytesIO()
-    img.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    return f"data:image/png;base64,{img_str}"
+# Path to horror.json
+JSON_PATH = "/var/www/html/horror.json"
 
-# Function for sentiment analysis
-def analyze_sentiment(text):
-    if not text.strip():
-        return "Please enter some text."
-    result = sentiment_analyzer(text)[0]
-    label = result['label']
-    score = result['score']
-    return f"Sentiment: {label} (Confidence: {score:.2%})"
+def write_test_json():
+    try:
+        # Test data to write
+        test_entry = [
+            {
+                "video_web_url": "test_horror_video.mp4",
+                "web_url": "test_horror_thumbnail.png",
+                "title": "TEST VIDEO ENTRY",
+                "theme": "horror",
+                "timestamp": datetime.now().isoformat()
+            }
+        ]
 
-# Chatbot-style feedback function
-def chatbot_response(user_feedback, chat_history):
-    if not user_feedback.strip():
-        return chat_history, "Please provide feedback."
-    # Append user feedback and a simple bot response
-    chat_history.append((user_feedback, f"Thanks for your feedback! I understood: '{user_feedback}'."))
-    return chat_history, ""
+        # Write to horror.json
+        logger.info(f"Attempting to write to {JSON_PATH}")
+        with open(JSON_PATH, "w") as f:
+            json.dump(test_entry, f, indent=4)
+        logger.info(f"✅ Successfully wrote test entry to {JSON_PATH}")
 
-# Main Gradio app using Blocks for advanced layout
-with gr.Blocks(css=".gradio-container {background-color: #f0f4f8;} .block {border-radius: 8px; padding: 10px;}") as demo:
-    gr.Markdown(
-        """
-        # Welcome to the Multi-Feature Gradio App! 🚀
-        This app combines **sentiment analysis**, **text-to-image generation**, and a **feedback chatbot**.
-        Built for Hugging Face Spaces (free tier, CPU-only).
-        """
-    )
-    
-    # Sentiment Analysis Section
-    with gr.Row():
-        with gr.Column(scale=2):
-            gr.Markdown("## 📝 Sentiment Analysis")
-            sentiment_input = gr.Textbox(
-                label="Enter text for sentiment analysis",
-                placeholder="Type something like 'I love coding!'",
-                lines=3
-            )
-            sentiment_button = gr.Button("Analyze Sentiment")
-            sentiment_output = gr.Textbox(label="Result", interactive=False)
-        with gr.Column(scale=1):
-            gr.Markdown("### Example Inputs")
-            gr.Examples(
-                examples=[
-                    "This app is amazing!",
-                    "I'm feeling a bit down today.",
-                    "Coding is challenging but fun!"
-                ],
-                inputs=sentiment_input
-            )
-    
-    # Text-to-Image Section
-    with gr.Row():
-        with gr.Column(scale=2):
-            gr.Markdown("## 🖼️ Text-to-Image Generation (Mock)")
-            image_prompt = gr.Textbox(
-                label="Enter a prompt for image generation",
-                placeholder="e.g., 'A colorful abstract pattern'",
-                lines=2
-            )
-            image_button = gr.Button("Generate Image")
-            image_output = gr.Image(label="Generated Image", type="pil")
-        with gr.Column(scale=1):
-            gr.Markdown("### Note")
-            gr.Markdown(
-                "This is a mock image generator (gradient-based) to keep it lightweight for the free tier."
-            )
-    
-    # Chatbot Feedback Section
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("## 💬 Feedback Chatbot")
-            chatbot = gr.Chatbot(label="Chat History")
-            feedback_input = gr.Textbox(
-                label="Your Feedback",
-                placeholder="Tell us what you think!",
-                lines=2
-            )
-            feedback_button = gr.Button("Submit Feedback")
-            feedback_output = gr.Textbox(label="Bot Response", interactive=False)
-    
-    # Event handlers
-    sentiment_button.click(
-        fn=analyze_sentiment,
-        inputs=sentiment_input,
-        outputs=sentiment_output
-    )
-    image_button.click(
-        fn=generate_mock_image,
-        inputs=image_prompt,
-        outputs=image_output
-    )
-    feedback_button.click(
-        fn=chatbot_response,
-        inputs=[feedback_input, chatbot],
-        outputs=[chatbot, feedback_output]
-    )
+        # Verify the write by reading back
+        with open(JSON_PATH, "r") as f:
+            data = json.load(f)
+        logger.info(f"Read back JSON data: {json.dumps(data, indent=2)}")
+
+    except Exception as e:
+        logger.error(f"❌ Failed to write to {JSON_PATH}: {str(e)}")
+        raise
 
-# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    write_test_json()