Update app.py

app.py

@@ -1,8 +1,15 @@
 import gradio as gr
-import os
+import torch
 from transformers import pipeline
+import os
 
-title = "📗Health and Mindful Story Gen❤️"
+# --- App Configuration ---
+title = "📗 Health and Mindful Story Gen ❤️"
+description = """
+Enter a topic or a starting sentence related to health, mindfulness, or well-being.
+The app will generate continuations from three different language models.
+**Note:** These models are very large. Initial loading and first-time generation may take a few minutes.
+"""
 
 # Define the examples to show in the interface
 examples = [
@@ -14,16 +21,16 @@ examples = [
     ["Alleviating stress"],
     ["Helping breathing, satisfaction"],
     ["Relieve Stress, Build Support"],
-    ["Relaxation Response"],
-    ["Deep Breaths"],
+    ["The Relaxation Response"],
+    ["Taking Deep Breaths"],
     ["Delete Not Helpful Thoughts"],
-    ["Strengthen Helpful"],
-    ["Reprogram Pain Stress Reactions"],
-    ["Sleep Better and Find Joy"],
-    ["Yoga Sleep"],
+    ["Strengthen Helpful Thoughts"],
+    ["Reprogram Pain and Stress Reactions"],
+    ["How to Sleep Better and Find Joy"],
+    ["Yoga for deep sleep"],
     ["Being a Happier and Healthier Person"],
-    ["Relieve Pain"],
-    ["Learn to Use Mindfulness to Affect Well Being"],
+    ["Relieve chronic pain by"],
+    ["Use Mindfulness to Affect Well Being"],
     ["Build and Boost Mental Strength"],
     ["Spending Time Outdoors"],
     ["Daily Routine Tasks"],
@@ -33,41 +40,92 @@ examples = [
     ["Feel better physically by"],
     ["Practicing mindfulness each day"],
     ["Be happier by"],
-    ["Meditation can improve health"],
-    ["Spending time outdoors"],
+    ["Meditation can improve health by"],
+    ["Spending time outdoors helps to"],
     ["Stress is relieved by quieting your mind, getting exercise and time with nature"],
     ["Break the cycle of stress and anxiety"],
     ["Feel calm in stressful situations"],
-    ["Deal with work pressure"],
-    ["Learn to reduce feelings of overwhelmed"]
+    ["Deal with work pressure by"],
+    ["Learn to reduce feelings of being overwhelmed"]
 ]
 
-# Initialize the models using Hugging Face pipelines
-generator1 = pipeline("text-generation", model="gpt2-large")
-generator2 = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
-generator3 = pipeline("text-generation", model="EleutherAI/gpt-j-6B")
+# --- Model Initialization ---
+# WARNING: Loading these models requires significant hardware (ideally a GPU with >24GB VRAM).
+# 'device_map="auto"' and 'torch_dtype' require the 'accelerate' library.
+# Install dependencies: pip install gradio transformers torch accelerate
+try:
+    print("Initializing models... This may take several minutes.")
+    
+    # Using device_map="auto" to automatically use available GPUs.
+    # Using torch_dtype="auto" to load models in half-precision (float16/bfloat16) to save memory.
+    
+    generator1 = pipeline("text-generation", model="gpt2-large", device_map="auto")
+    print("GPT-2 Large loaded.")
+    
+    generator2 = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B", torch_dtype="auto", device_map="auto")
+    print("GPT-Neo 2.7B loaded.")
+
+    generator3 = pipeline("text-generation", model="EleutherAI/gpt-j-6B", torch_dtype="auto", device_map="auto")
+    print("GPT-J 6B loaded.")
+
+    print("All models loaded successfully! ✅")
+
+except Exception as e:
+    print(f"Error loading models: {e}")
+    print("Please ensure you have 'torch' and 'accelerate' installed and have sufficient VRAM.")
+    # Create dummy functions if models fail to load, so the app can still launch.
+    def failed_generator(prompt, **kwargs):
+        return [{'generated_text': "Model failed to load. Check console for errors."}]
+    generator1 = generator2 = generator3 = failed_generator
 
-with gr.Blocks() as demo:
-    gr.Markdown(f"# {title}")
-    input_textbox = gr.Textbox(lines=5, label="Enter a sentence to get another sentence.")
+
+# --- App Logic ---
+def generate_outputs(input_text: str) -> tuple[str, str, str]:
+    """Generates text from the three loaded models."""
+    # Using 'max_new_tokens' is preferred over 'max_length' to specify the length of the generated text only.
+    params = {"max_new_tokens": 60, "num_return_sequences": 1}
+    
+    out1 = generator1(input_text, **params)[0]['generated_text']
+    out2 = generator2(input_text, **params)[0]['generated_text']
+    out3 = generator3(input_text, **params)[0]['generated_text']
+    
+    return out1, out2, out3
+
+# --- Gradio Interface ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
+    gr.Markdown(description)
 
     with gr.Row():
-        gen1_output = gr.Textbox(label="GPT-2 Large Output")
-        gen2_output = gr.Textbox(label="GPT-Neo Output")
-        gen3_output = gr.Textbox(label="GPT-J Output")
+        with gr.Column(scale=1):
+            input_area = gr.TextArea(
+                lines=3,
+                label="Your starting prompt 👇",
+                placeholder="e.g., 'To relieve stress, I will try...'"
+            )
+            generate_button = gr.Button("Generate ✨", variant="primary")
+    
+        with gr.Column(scale=2):
+            with gr.Tabs():
+                with gr.TabItem("GPT-2 Large"):
+                    gen1_output = gr.TextArea(label="GPT-2 Large Output", interactive=False, lines=7)
+                with gr.TabItem("GPT-Neo 2.7B"):
+                    gen2_output = gr.TextArea(label="GPT-Neo 2.7B Output", interactive=False, lines=7)
+                with gr.TabItem("GPT-J 6B"):
+                    gen3_output = gr.TextArea(label="GPT-J 6B Output", interactive=False, lines=7)
 
-    def generate_outputs(input_text):
-        out1 = generator1(input_text, max_length=50)[0]['generated_text']
-        out2 = generator2(input_text, max_length=50)[0]['generated_text']
-        out3 = generator3(input_text, max_length=50)[0]['generated_text']
-        return out1, out2, out3
+    gr.Examples(
+        examples=examples,
+        inputs=input_area,
+        label="Example Prompts (Click to use)"
+    )
 
-    gr.Button("Generate").click(
+    generate_button.click(
         fn=generate_outputs,
-        inputs=input_textbox,
-        outputs=[gen1_output, gen2_output, gen3_output]
+        inputs=input_area,
+        outputs=[gen1_output, gen2_output, gen3_output],
+        api_name="generate"
     )
 
-    gr.Examples(examples=examples, inputs=input_textbox, examples_per_page=100)
-
-demo.launch(share=False)
+if __name__ == "__main__":
+    demo.launch()