feat: switch to DistilGPT-2 text generation

- Replace image generation with text generation
- Add DistilGPT-2 model for better CPU performance
- Add temperature and length controls
- Update documentation and requirements
- Optimize for CPU usage
- Add copy text functionality

README.md

@@ -1,25 +1,25 @@
 ---
-title: AI Text Generator
-emoji: 💬
-colorFrom: blue
-colorTo: green
+title: Health Diet Planner
+emoji: 🏥
+colorFrom: green
+colorTo: blue
 sdk: streamlit
 sdk_version: 1.41.1
 app_file: app.py
 pinned: false
-short_description: Fast text generation using DistilGPT-2
+short_description: AI-powered health diet recommendations
 ---
 
-# AI Text Generator
-Quick text generation using DistilGPT-2
+# Health Diet Planner
+AI-assisted diet planning based on health conditions
 
 ## Features
-- Fast text generation
-- CPU-optimized performance
-- Adjustable creativity settings
-- Memory efficient (< 2GB)
+- Personalized diet recommendations
+- Health condition specific advice
+- Dietary restriction support
+- Downloadable diet plans
 
-## Usage Tips
-- Clear, specific prompts work best
-- Adjust temperature for different styles
-- Experiment with prompt formats
+## Important Notice
+- For reference purposes only
+- Consult healthcare professionals
+- Not a substitute for medical advice

app.py

@@ -2,72 +2,131 @@ import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 import gc
+from functools import lru_cache
 
 @st.cache_resource
 def load_model():
-    # Load DistilGPT-2
-    model_id = "distilgpt2"
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    # Force CPU and optimize memory
+    torch.set_num_threads(6)  # Increased threads for better CPU utilization
+    torch.set_grad_enabled(False)  # Disable gradients
+    
+    model_id = "microsoft/BioGPT-Large"
+    
+    # Load tokenizer with caching
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_id,
+        model_max_length=512,
+        padding_side='left'
+    )
+    
+    # Optimized model loading
     model = AutoModelForCausalLM.from_pretrained(
         model_id,
         torch_dtype=torch.float32,
-        low_cpu_mem_usage=True
-    ).to("cpu")
+        low_cpu_mem_usage=True,
+        device_map='cpu',
+        max_memory={'cpu': '16GB'}
+    )
     
-    # Set threading and memory optimizations
-    torch.set_num_threads(4)
-    gc.collect()
+    # Model optimizations
+    model.eval()  # Set to evaluation mode
+    model = torch.compile(model)  # Use torch compile for faster inference
     
+    gc.collect()
     return model, tokenizer
 
-st.title("💬 AI Text Generator")
-st.write("Generate creative text using DistilGPT-2")
+@lru_cache(maxsize=32)  # Cache recent generations
+def generate_diet_plan(health_condition: str, dietary_restrictions: str, cache_key: str):
+    model, tokenizer = load_model()
+    
+    prompt = f"""
+Provide a concise diet plan for {health_condition} with {dietary_restrictions} restrictions.
+Key points:
+1. Recommended foods
+2. Foods to avoid
+3. Meal schedule
+"""
+    
+    try:
+        with torch.inference_mode():
+            inputs = tokenizer(
+                prompt, 
+                return_tensors="pt",
+                max_length=256,  # Reduced for faster processing
+                truncation=True,
+                padding=True
+            )
+            
+            outputs = model.generate(
+                inputs["input_ids"],
+                max_length=512,  # Reduced length
+                temperature=0.7,
+                num_return_sequences=1,
+                pad_token_id=tokenizer.eos_token_id,
+                do_sample=True,
+                top_p=0.9,
+                repetition_penalty=1.2
+            )
+            
+            return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    finally:
+        # Clean up
+        gc.collect()
+        torch.cuda.empty_cache() if torch.cuda.is_available() else None
 
-# Initialize model
-model, tokenizer = load_model()
+# Streamlit interface
+st.title("🏥 Health Diet Planner")
 
-# User input
-prompt = st.text_area(
-    "Enter your prompt:",
-    "Once upon a time in a digital world,"
-)
+st.write("Fast diet recommendations based on health conditions")
 
-# Generation settings
-with st.sidebar:
-    max_length = st.slider("Max Length", 50, 200, 100)
-    temperature = st.slider("Temperature", 0.1, 1.0, 0.7)
+# User inputs with validation
+health_condition = st.text_input(
+    "Health Condition",
+    placeholder="e.g., Diabetes, Hypertension"
+).strip()
 
-if st.button("Generate Text"):
-    with st.spinner("Generating text..."):
-        try:
-            # Tokenize and generate
-            inputs = tokenizer(prompt, return_tensors="pt")
-            
-            with torch.inference_mode():
-                outputs = model.generate(
-                    inputs["input_ids"],
-                    max_length=max_length,
-                    temperature=temperature,
-                    num_return_sequences=1,
-                    pad_token_id=tokenizer.eos_token_id,
-                    do_sample=True,
+dietary_restrictions = st.text_input(
+    "Dietary Restrictions",
+    placeholder="e.g., Vegetarian, No nuts"
+).strip()
+
+if st.button("Generate Diet Plan"):
+    if health_condition:
+        with st.spinner("Generating plan (typically 15-30 seconds)..."):
+            try:
+                # Create cache key
+                cache_key = f"{health_condition}_{dietary_restrictions}"
+                diet_plan = generate_diet_plan(
+                    health_condition, 
+                    dietary_restrictions,
+                    cache_key
                 )
-            
-            # Decode and display
-            generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            st.write("### Generated Text:")
-            st.write(generated_text)
-            
-            # Add copy button
-            st.button("📋 Copy Text", 
-                     on_click=lambda: st.write(generated_text))
-            
-        except Exception as e:
-            st.error(f"Error: {str(e)}")
+                
+                st.markdown(f"### Diet Plan for {health_condition}")
+                st.markdown(diet_plan)
+                
+                # Download option
+                st.download_button(
+                    "💾 Download Plan",
+                    diet_plan,
+                    file_name="diet_plan.txt"
+                )
+                
+            except Exception as e:
+                st.error("Error generating plan. Try simpler input.")
+    else:
+        st.warning("Please enter a health condition")
+
+# Clear cache button in sidebar
+if st.sidebar.button("Clear Cache"):
+    generate_diet_plan.cache_clear()
+    st.cache_resource.clear()
+    st.success("Cache cleared!")
 
 st.markdown("""
-### Tips for better results:
-- Start with clear, well-structured prompts
-- Adjust temperature for creativity (higher) or consistency (lower)
-- Try different prompt styles for different outputs
+### Important Notes:
+- This is an AI-generated diet plan for reference only
+- Always consult healthcare professionals before making dietary changes
+- Individual needs may vary
+- Update your health condition details for more accurate recommendations
 """)

requirements.txt

@@ -2,5 +2,6 @@
 # streamlit is already pre-installed
 streamlit
 torch
-transformers
-accelerate
+transformers>=4.34.0
+accelerate
+scikit-learn