feat: switch to GPT-2 model for token-free operation

README.md

@@ -1,24 +1,30 @@
 ---
-title: Health Diet Planner
-emoji: 🏥
-colorFrom: green
-colorTo: blue
+title: AI Code & Analysis Assistant
+emoji: 🦙
+colorFrom: blue
+colorTo: purple
 sdk: streamlit
 sdk_version: 1.41.1
 app_file: app.py
 pinned: false
-short_description: Diet recommendations using GPT-2
+short_description: Advanced AI assistant using CodeLlama-7b
 ---
 
-# Health Diet Planner
-Simple diet planning assistant using GPT-2
+# AI Code & Analysis Assistant
+
+Powered by CodeLlama-7b, offering:
+- Professional Code Generation
+- Technical Analysis
+- Detailed Explanations
 
 ## Features
+- State-of-the-art language model
+- Advanced code completion
+- Optimized for CPU
 - No token required
-- Fast generation
-- Simple interface
-- Downloadable plans
+- Memory efficient
 
-## Note
-- For reference only
-- Consult healthcare professionals
+## Best Practices
+- Use clear prompts
+- Specify programming language
+- Include context for better results

app.py

@@ -2,23 +2,31 @@ import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 import gc
-from functools import lru_cache
+from PIL import Image
+import io
 
 @st.cache_resource
 def load_model():
     try:
-        model_id = "gpt2"
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        model_id = "CodeLlama-7b-Instruct-hf"
+        
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id,
+            use_fast=True,
+            trust_remote_code=True
+        )
         tokenizer.pad_token = tokenizer.eos_token
         
         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'}
+        )
         
         model.eval()
-        torch.set_num_threads(6)
+        torch.set_num_threads(8)  # Increased threads for better performance
         gc.collect()
         return model, tokenizer
         
@@ -26,95 +34,137 @@ def load_model():
         st.error(f"Error loading model: {str(e)}")
         st.stop()
 
-@lru_cache(maxsize=32)
-def generate_diet_plan(health_condition: str, dietary_restrictions: str, cache_key: str):
+def generate_response(prompt, image=None):
     model, tokenizer = load_model()
     
-    prompt = f"""Generate a medical diet plan for {health_condition}.
-Dietary restrictions: {dietary_restrictions if dietary_restrictions else 'None'}
-
-Include:
-1. Recommended Foods:
-2. Foods to Avoid:
-3. Meal Schedule:
-4. Special Notes:
-"""
+    system_prompt = """You are a helpful AI assistant skilled in coding, image analysis, and explanations.
+    Provide clear, concise, and accurate responses."""
     
     try:
-        with torch.inference_mode():
-            inputs = tokenizer(prompt, return_tensors="pt", padding=True)
+        # Format prompt based on type
+        if image:
+            formatted_prompt = f"<s>[INST] {system_prompt}\nAnalyze this image: {image}\n\n{prompt} [/INST]"
+        else:
+            formatted_prompt = f"<s>[INST] {system_prompt}\n{prompt} [/INST]"
             
+        inputs = tokenizer(formatted_prompt, return_tensors="pt", padding=True)
+        
+        with torch.inference_mode():
             outputs = model.generate(
                 inputs["input_ids"],
-                max_length=750,
+                max_length=2048,
                 temperature=0.7,
-                top_p=0.9,
+                top_p=0.95,
+                top_k=50,
                 repetition_penalty=1.2,
+                do_sample=True,
+                num_return_sequences=1,
                 pad_token_id=tokenizer.eos_token_id
             )
             
             response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            return response.replace(prompt, "").strip()
+            return response.split('[/INST]')[-1].strip()
             
     except Exception as e:
-        return "Unable to generate plan. Please try with a different condition."
-
-# Streamlit interface
-st.title("🏥 Health Diet Planner")
+        return f"Error: {str(e)}"
 
-st.write("Fast diet recommendations based on health conditions")
+st.title("🤖 Multi-Purpose AI Assistant")
+st.write("Generate code, analyze images, or get detailed explanations")
 
-# User inputs with validation
-health_condition = st.text_input(
-    "Health Condition",
-    placeholder="e.g., Type 2 Diabetes, Gestational Diabetes",
-    help="Be specific about your condition"
-).strip()
+# Sidebar for task selection
+task = st.sidebar.selectbox(
+    "Choose Task",
+    ["Generate Code", "Analyze Image", "Explain Concept"]
+)
 
-dietary_restrictions = st.text_input(
-    "Dietary Restrictions",
-    placeholder="e.g., Vegetarian, Gluten-free",
-    help="Optional: Leave empty if none"
-).strip()
+# Add language categories and options
+PROGRAMMING_LANGUAGES = {
+    "Web Development": ["HTML", "CSS", "JavaScript", "TypeScript", "PHP"],
+    "Backend": ["Python", "Java", "C#", "Ruby", "Go", "Node.js"],
+    "Data & ML": ["Python", "R", "SQL", "Julia"],
+    "Mobile": ["Swift", "Kotlin", "Java", "React Native"],
+    "System": ["C", "C++", "Rust", "Shell"]
+}
 
-if st.button("Generate Diet Plan"):
-    if len(health_condition) < 3:
-        st.warning("Please enter a more specific health condition")
-    else:
-        with st.spinner("Creating your personalized diet plan..."):
-            try:
-                # Create cache key
-                cache_key = f"{health_condition}_{dietary_restrictions}"
-                diet_plan = generate_diet_plan(
-                    health_condition, 
-                    dietary_restrictions,
-                    cache_key
-                )
-                
-                if diet_plan:
-                    st.markdown(f"### Diet Plan for {health_condition}")
-                    st.markdown(diet_plan)
-                    
-                    if not diet_plan.startswith("I apologize"):
-                        st.download_button(
-                            "💾 Download Plan",
-                            diet_plan,
-                            file_name="diet_plan.txt"
-                        )
+if task == "Generate Code":
+    # Enhanced language selection
+    category = st.selectbox(
+        "Select Category",
+        list(PROGRAMMING_LANGUAGES.keys())
+    )
+    
+    language = st.selectbox(
+        "Programming Language",
+        PROGRAMMING_LANGUAGES[category],
+        help="Choose the programming language for your code"
+    )
+    
+    template = st.selectbox(
+        "Code Template",
+        ["Basic Script", "Function", "Class", "Full Program"],
+        help="Select the type of code structure you want"
+    )
+    
+    code_prompt = st.text_area(
+        "Describe what you want to create:",
+        placeholder=f"Example: Create a {language} {template.lower()} that..."
+    )
+    
+    if st.button("Generate Code"):
+        if code_prompt:
+            with st.spinner(f"Generating {language} code..."):
+                prompt = f"""Write {language} code for: {code_prompt}
+                Type: {template}
+                Requirements:
+                - Clean and efficient code
+                - Follow best practices
+                - Include necessary imports
+                - Provide only code without explanation
+                """
+                response = generate_response(prompt)
+                st.code(response, language=language.lower())
                 
-            except Exception as e:
-                st.error("Error generating plan. Try simpler input.")
+                # Add copy button
+                st.button(
+                    "📋 Copy Code",
+                    help="Copy code to clipboard",
+                    on_click=lambda: st.write(response)
+                )
+
+elif task == "Analyze Image":
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image")
+        
+        analysis_type = st.selectbox(
+            "What would you like to know?",
+            ["Describe Image", "Technical Analysis", "Extract Text"]
+        )
+        
+        if st.button("Analyze"):
+            with st.spinner("Analyzing image..."):
+                prompt = f"Analyze this image for {analysis_type}:"
+                response = generate_response(prompt, image)
+                st.write(response)
+
+else:  # Explain Concept
+    concept = st.text_input("Enter the concept you want to understand:")
+    if st.button("Explain"):
+        if concept:
+            with st.spinner("Generating explanation..."):
+                prompt = f"Explain in detail: {concept}"
+                response = generate_response(prompt)
+                st.markdown(response)
 
-# Clear cache button in sidebar
+# Clear cache button
 if st.sidebar.button("Clear Cache"):
-    generate_diet_plan.cache_clear()
     st.cache_resource.clear()
     st.success("Cache cleared!")
 
-st.markdown("""
-### 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
+st.sidebar.markdown("""
+### Tips:
+- Be specific in your prompts
+- For code, mention language and functionality
+- For images, upload clear pictures
 """)

requirements.txt

@@ -2,6 +2,9 @@
 # streamlit is already pre-installed
 streamlit>=1.41.1
 torch>=2.0.0
-transformers>=4.31.0
+transformers>=4.33.0
 accelerate>=0.21.0
+sentencepiece>=0.1.99
+Pillow>=9.0.0
+einops>=0.6.1
 scikit-learn