Update app.py

app.py

@@ -1,48 +1,88 @@
 import gradio as gr
 from transformers import pipeline
+import torch
 
-# Initialize pipelines for generation and sentiment
-haiku_generator = pipeline("text2text-generation", model="google/flan-t5-small")
-sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+# Initialize the pipeline with a small, efficient instruction-tuned model
+# This model is specifically chosen for its small footprint and CPU compatibility
+try:
+    generator = pipeline(
+        "text-generation", 
+        model="MBZUAI/LaMini-GPT-124M",
+        device="cpu"
+    )
+except Exception as e:
+    generator = None
 
-def generate_zen_output(text):
-    if not text.strip():
-        return "Please enter a thought.", "No sentiment detected."
-    
-    # Generate Haiku using a prompt-based approach
-    haiku_prompt = f"Write a haiku about: {text}"
-    haiku_result = haiku_generator(haiku_prompt, max_length=50, num_beams=5)[0]['generated_text']
+def generate_meditation(mood, focus_topic):
+    if generator is None:
+        return "Model is still loading, please try again in a moment."
     
-    # Analyze Sentiment
-    sentiment_result = sentiment_analyzer(text)[0]
-    label = sentiment_result['label']
-    score = sentiment_result['score']
+    # Constructing a structured prompt for the instruction-tuned model
+    prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a calming, 3-sentence meditation script for someone feeling {mood}. The meditation should focus on the theme of {focus_topic}. Use soothing language.\n\n### Response:"
     
-    mood_indicator = f"{label} energy ({round(score * 100, 1)}% confidence)"
-    return haiku_result, mood_indicator
+    try:
+        result = generator(
+            prompt, 
+            max_new_tokens=150, 
+            num_return_sequences=1, 
+            temperature=0.7,
+            do_sample=True
+        )
+        
+        full_text = result[0]['generated_text']
+        # Extract the text after the response marker
+        if "### Response:" in full_text:
+            response = full_text.split("### Response:")[1].strip()
+        else:
+            response = full_text
+            
+        return response
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
-# UI Styling
-css = ".gradio-container {background-color: #fcfaf2; font-family: 'Georgia', serif;} .main-title {text-align: center; color: #4a5568;}"
+# Custom CSS for a peaceful aesthetic
+custom_css = """
+.gradio-container {background-color: #f0f4f8}
+#title-text {text-align: center; color: #2d5a27}
+"""
 
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("# 🌸 Zen Garden AI", elem_classes="main-title")
-    gr.Markdown("Transform your thoughts into haikus and visualize the emotional atmosphere of your words.")
+# Build the Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
+    gr.Markdown("# 🌿 AI Zen Garden", elem_id="title-text")
+    gr.Markdown("**Daily AI Challenge - 4/2/2026**")
+    gr.Markdown("Take a deep breath. Tell the AI how you feel, and let it guide you through a moment of peace.")
     
     with gr.Row():
         with gr.Column():
-            input_box = gr.Textbox(placeholder="Describe a feeling, a place, or a moment...", label="Your Input", lines=3)
-            submit_btn = gr.Button("Seek Stillness", variant="primary")
+            mood_input = gr.Dropdown(
+                choices=["Anxious", "Tired", "Restless", "Sad", "Overwhelmed", "Neutral"], 
+                label="Current Mood", 
+                value="Anxious"
+            )
+            focus_input = gr.Textbox(
+                label="Focus Topic", 
+                placeholder="e.g., Summer Rain, Forest Silence, Soft Clouds...",
+                value="Mountain Air"
+            )
+            submit_btn = gr.Button("Generate Meditation", variant="primary")
         
         with gr.Column():
-            output_haiku = gr.Textbox(label="Generated Haiku", interactive=False)
-            output_mood = gr.Label(label="Atmospheric Reading")
-            
-    submit_btn.click(fn=generate_zen_output, inputs=input_box, outputs=[output_haiku, output_mood])
+            output_text = gr.Textbox(
+                label="Your Personalized Script", 
+                lines=8, 
+                interactive=False
+            )
     
     gr.Examples(
-        examples=["A quiet morning by the lake", "The chaotic energy of a big city", "Finding a forgotten letter in a book"],
-        inputs=input_box
+        examples=[["Overwhelmed", "Ocean Waves"], ["Tired", "Warm Sunlight"]],
+        inputs=[mood_input, focus_input]
+    )
+    
+    submit_btn.click(
+        fn=generate_meditation, 
+        inputs=[mood_input, focus_input], 
+        outputs=output_text
     )
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     demo.launch()