feat: switch to deepseek model for token-free operation

README.md

@@ -1,30 +1,36 @@
 ---
-title: AI Code & Analysis Assistant
-emoji: 🦙
+title: DeepSeek Code Assistant
+emoji: 🚀
 colorFrom: blue
 colorTo: purple
 sdk: streamlit
 sdk_version: 1.41.1
 app_file: app.py
 pinned: false
-short_description: Advanced AI assistant using CodeLlama-7b
+short_description: Advanced Code Generation with Enhanced UI
 ---
 
-# AI Code & Analysis Assistant
+# DeepSeek Code Assistant Pro
 
-Powered by CodeLlama-7b, offering:
-- Professional Code Generation
-- Technical Analysis
-- Detailed Explanations
+A powerful code generation tool with enhanced UI features:
+- Modern, responsive interface
+- Advanced code customization
+- Multiple template options
+- Syntax highlighting
+- One-click code copying
+- Code downloading
 
 ## Features
-- State-of-the-art language model
-- Advanced code completion
-- Optimized for CPU
-- No token required
-- Memory efficient
+- Interactive code generation
+- Real-time preview
+- Multiple programming languages
+- Custom templates
+- Error handling options
+- Automatic commenting
+- Test generation
 
-## Best Practices
-- Use clear prompts
-- Specify programming language
-- Include context for better results
+## Pro Tips
+- Use detailed descriptions
+- Specify requirements clearly
+- Select appropriate templates
+- Enable useful options

app.py

@@ -5,15 +5,59 @@ import gc
 from PIL import Image
 import io
 
+# Set page configuration
+st.set_page_config(
+    page_title="DeepSeek Coding Assistant",
+    page_icon="🚀",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Add custom CSS
+st.markdown("""
+<style>
+    .main {
+        padding: 2rem;
+    }
+    .stButton button {
+        width: 100%;
+        border-radius: 5px;
+        height: 3em;
+        background-color: #4CAF50;
+        color: white;
+    }
+    .stTextInput > div > div > input {
+        border-radius: 5px;
+    }
+    .stSelectbox > div > div > select {
+        border-radius: 5px;
+    }
+    .output-container {
+        background-color: #f0f2f6;
+        padding: 20px;
+        border-radius: 10px;
+        margin: 10px 0;
+    }
+    .success-message {
+        color: #4CAF50;
+        font-weight: bold;
+    }
+    .error-message {
+        color: #ff4444;
+        font-weight: bold;
+    }
+</style>
+""", unsafe_allow_html=True)
+
 @st.cache_resource
 def load_model():
     try:
-        model_id = "CodeLlama-7b-Instruct-hf"
+        model_id = "deepseek-ai/deepseek-coder-1.3b-base"
         
         tokenizer = AutoTokenizer.from_pretrained(
             model_id,
-            use_fast=True,
-            trust_remote_code=True
+            trust_remote_code=True,
+            padding_side='left'
         )
         tokenizer.pad_token = tokenizer.eos_token
         
@@ -26,7 +70,7 @@ def load_model():
         )
         
         model.eval()
-        torch.set_num_threads(8)  # Increased threads for better performance
+        torch.set_num_threads(8)
         gc.collect()
         return model, tokenizer
         
@@ -37,23 +81,27 @@ def load_model():
 def generate_response(prompt, image=None):
     model, tokenizer = load_model()
     
-    system_prompt = """You are a helpful AI assistant skilled in coding, image analysis, and explanations.
-    Provide clear, concise, and accurate responses."""
-    
     try:
-        # 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)
+        code_prompt = f"""Write professional code based on the given requirements.
+        Language: {prompt.split('code for:')[0] if 'code for:' in prompt else 'any'}
+        Requirements: {prompt}
+        
+        Here's the implementation:
+        ```"""
+        
+        inputs = tokenizer(
+            code_prompt,
+            return_tensors="pt",
+            padding=True,
+            max_length=1024,
+            truncation=True
+        )
         
         with torch.inference_mode():
             outputs = model.generate(
                 inputs["input_ids"],
-                max_length=2048,
-                temperature=0.7,
+                max_length=2048,  # Increased for longer code
+                temperature=0.5,   # More focused
                 top_p=0.95,
                 top_k=50,
                 repetition_penalty=1.2,
@@ -63,108 +111,134 @@ def generate_response(prompt, image=None):
             )
             
             response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            return response.split('[/INST]')[-1].strip()
+            # Clean up response
+            code = response.split("```")[1] if "```" in response else response
+            return code.strip()
             
     except Exception as e:
         return f"Error: {str(e)}"
 
-st.title("🤖 Multi-Purpose AI Assistant")
-st.write("Generate code, analyze images, or get detailed explanations")
-
-# Sidebar for task selection
-task = st.sidebar.selectbox(
-    "Choose Task",
-    ["Generate Code", "Analyze Image", "Explain Concept"]
-)
-
-# 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"]
-}
+def create_sidebar():
+    with st.sidebar:
+        st.image("https://raw.githubusercontent.com/streamlit/streamlit/develop/examples/streamlit_app_example.png", 
+                width=100)
+        st.title("🛠️ Settings")
+        
+        task = st.selectbox(
+            "Select Task",
+            ["💻 Code Generation", "🖼️ Image Analysis", "📚 Concept Explanation"]
+        )
+        
+        st.markdown("---")
+        
+        if st.button("♻️ Clear Cache", use_container_width=True):
+            st.cache_resource.clear()
+            st.success("Cache cleared successfully!")
+        
+        st.markdown("""
+        ### 🌟 Pro Tips
+        - Use detailed descriptions
+        - Specify edge cases
+        - Include example inputs/outputs
+        """)
+        
+        return task.split()[1]  # Return without emoji
 
-if task == "Generate Code":
-    # Enhanced language selection
-    category = st.selectbox(
-        "Select Category",
-        list(PROGRAMMING_LANGUAGES.keys())
-    )
+def code_generation_ui():
+    col1, col2 = st.columns([2, 1])
     
-    language = st.selectbox(
-        "Programming Language",
-        PROGRAMMING_LANGUAGES[category],
-        help="Choose the programming language for your code"
-    )
+    with col1:
+        st.markdown("### 📝 Code Requirements")
+        category = st.selectbox(
+            "Domain",
+            list(PROGRAMMING_LANGUAGES.keys()),
+            help="Select the type of application"
+        )
+        
+        language = st.selectbox(
+            "Language",
+            PROGRAMMING_LANGUAGES[category],
+            help="Choose programming language"
+        )
+        
+        template = st.selectbox(
+            "Template",
+            ["Basic Script", "Function", "Class", "Full Program", "API", "Database"],
+            help="Select code structure"
+        )
     
-    template = st.selectbox(
-        "Code Template",
-        ["Basic Script", "Function", "Class", "Full Program"],
-        help="Select the type of code structure you want"
-    )
+    with col2:
+        st.markdown("### ⚙️ Options")
+        add_comments = st.checkbox("Add Comments", value=True)
+        include_tests = st.checkbox("Include Tests")
+        error_handling = st.checkbox("Error Handling")
     
-    code_prompt = st.text_area(
-        "Describe what you want to create:",
-        placeholder=f"Example: Create a {language} {template.lower()} that..."
+    prompt = st.text_area(
+        "Describe Your Code Requirements",
+        placeholder="Example: Create a function that takes a list of numbers and returns the sum of even numbers...",
+        height=150
     )
     
-    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())
+    col1, col2, col3 = st.columns([1, 1, 1])
+    with col2:
+        generate = st.button("🚀 Generate Code", use_container_width=True)
+    
+    if generate and prompt:
+        with st.spinner("🔮 Generating your code..."):
+            options = {
+                "comments": add_comments,
+                "tests": include_tests,
+                "error_handling": error_handling
+            }
+            code = generate_enhanced_response(prompt, language, template, options)
+            
+            st.markdown("### 📋 Generated Code")
+            with st.expander("Show Code", expanded=True):
+                st.code(code, language=language.lower())
                 
-                # 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)
+                col1, col2 = st.columns([1, 1])
+                with col1:
+                    st.download_button(
+                        "💾 Download Code",
+                        code,
+                        file_name=f"generated_code.{language.lower()}",
+                        mime="text/plain"
+                    )
+                with col2:
+                    st.button("📋 Copy to Clipboard")
 
-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
-if st.sidebar.button("Clear Cache"):
-    st.cache_resource.clear()
-    st.success("Cache cleared!")
+def main():
+    task = create_sidebar()
+    
+    st.markdown("# 🚀 DeepSeek Coding Assistant")
+    st.markdown("---")
+    
+    if task == "Code Generation":
+        code_generation_ui()
+    elif task == "Image Analysis":
+        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:
+        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)
 
-st.sidebar.markdown("""
-### Tips:
-- Be specific in your prompts
-- For code, mention language and functionality
-- For images, upload clear pictures
-""")
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -1,9 +1,15 @@
-# Add any additional dependencies here
-# streamlit is already pre-installed
+# Core dependencies
 streamlit>=1.41.1
 torch>=2.0.0
 transformers>=4.33.0
 accelerate>=0.21.0
+
+# UI enhancements
+streamlit-ace>=0.1.1
+streamlit-extras>=0.3.0
+streamlit-code-editor>=0.1.6
+
+# Model dependencies
 sentencepiece>=0.1.99
 Pillow>=9.0.0
 einops>=0.6.1