Upload llm_app.py

llm_app.py

@@ -6,17 +6,157 @@ from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.vectorstores import Chroma
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
-import requests
-import json
+import anthropic
 import base64
 from PIL import Image
 import io
 import re
+import random
 from dotenv import load_dotenv
 
 # Load environment variables from .env file
 load_dotenv()
 
+# --- Code Practice Assistant ---
+
+class CodePracticeAssistant:
+    def __init__(self):
+        self.anthropic_client = None
+        self._setup_llm()
+        
+    def _setup_llm(self):
+        """Setup Claude LLM for code practice"""
+        try:
+            self.anthropic_client = anthropic.Anthropic(
+                api_key=os.environ.get("ANTHROPIC_KEY")
+            )
+            print("✅ Code Practice LLM setup successful!")
+        except Exception as e:
+            print(f"❌ Error setting up Code Practice LLM: {e}")
+            self.anthropic_client = None
+
+    def generate_practice_problem(self, topic, problem_type):
+        """Generate a practice problem based on topic and type"""
+        if not self.anthropic_client:
+            return "LLM not available. Please check your API key.", ""
+        
+        # Map dropdown choices to internal problem types
+        problem_type_mapping = {
+            "Create Practice Problems": "create",
+            "Debug - Identify Error Type": "debug_error_type", 
+            "Debug - Explain Error Reason": "debug_error_reason",
+            "Debug - Fix the Error": "debug_fix",
+            "Optimize Code Performance": "optimize"
+        }
+        
+        internal_type = problem_type_mapping.get(problem_type, "create")
+        
+        problem_types = {
+            "create": "Create a coding problem where students need to write code from scratch",
+            "debug_error_type": "Create a coding problem with a bug where students need to identify what type of error it is",
+            "debug_error_reason": "Create a coding problem with a bug where students need to explain why the error occurs",
+            "debug_fix": "Create a coding problem with a bug where students need to fix the code",
+            "optimize": "Create a coding problem where students need to optimize/improve the code performance"
+        }
+        
+        prompt = f"""Create a programming practice problem for a student learning {topic}.
+
+Problem Type: {problem_types.get(internal_type, internal_type)}
+
+Requirements:
+- Make it appropriate for beginners to intermediate level
+- Include clear instructions
+- Provide a specific, focused problem
+- If it's a debug problem, include the buggy code
+- If it's an optimization problem, provide the original code
+- Make it engaging and educational
+
+Format your response as:
+PROBLEM: [The problem description and requirements]
+CODE: [Any starter code if applicable, or "Write your code here:"]
+
+Keep it concise but clear."""
+
+        try:
+            response = self.anthropic_client.messages.create(
+                model="claude-3-5-haiku-20241022",
+                max_tokens=1000,
+                temperature=0.7,
+                messages=[{"role": "user", "content": prompt}]
+            )
+            
+            result = response.content[0].text.strip()
+            
+            # Parse the response to separate problem and code
+            if "PROBLEM:" in result and "CODE:" in result:
+                parts = result.split("CODE:")
+                problem = parts[0].replace("PROBLEM:", "").strip()
+                code = parts[1].strip() if len(parts) > 1 else ""
+            else:
+                problem = result
+                code = ""
+            
+            return problem, code
+            
+        except Exception as e:
+            return f"Error generating problem: {str(e)}", ""
+
+    def analyze_student_code(self, topic, problem_type, problem_description, student_code):
+        """Analyze student's code and provide feedback"""
+        if not self.anthropic_client:
+            return "LLM not available. Please check your API key."
+        
+        # Map dropdown choices to internal problem types
+        problem_type_mapping = {
+            "Create Practice Problems": "create",
+            "Debug - Identify Error Type": "debug_error_type", 
+            "Debug - Explain Error Reason": "debug_error_reason",
+            "Debug - Fix the Error": "debug_fix",
+            "Optimize Code Performance": "optimize"
+        }
+        
+        internal_type = problem_type_mapping.get(problem_type, "create")
+        
+        analysis_types = {
+            "create": "Evaluate the code for correctness, completeness, and best practices",
+            "debug_error_type": "Identify what type of error the code has and explain it",
+            "debug_error_reason": "Explain why the error occurs in the code",
+            "debug_fix": "Provide the corrected code and explain the fixes",
+            "optimize": "Suggest optimizations and explain how they improve performance"
+        }
+        
+        prompt = f"""Analyze this student's code for a {topic} practice problem.
+
+Problem Type: {problem_type}
+Problem Description: {problem_description}
+
+Student's Code:
+{student_code}
+
+Analysis Type: {analysis_types.get(internal_type, "General analysis")}
+
+Please provide:
+1. A detailed analysis of their code
+2. What they did well
+3. Areas for improvement
+4. If applicable, the correct solution or fixes
+5. Helpful tips and explanations
+
+Be encouraging but honest. Focus on learning and improvement."""
+
+        try:
+            response = self.anthropic_client.messages.create(
+                model="claude-3-5-haiku-20241022",
+                max_tokens=1500,
+                temperature=0.7,
+                messages=[{"role": "user", "content": prompt}]
+            )
+            
+            return response.content[0].text.strip()
+            
+        except Exception as e:
+            return f"Error analyzing code: {str(e)}"
+
 # --- LLM-Powered Curriculum Assistant ---
 
 class LLMCurriculumAssistant:
@@ -77,35 +217,23 @@ class LLMCurriculumAssistant:
         print("✅ Vector database built successfully")
 
     def _setup_llm(self):
-        """Setup DeepSeek LLM"""
+        """Setup Claude LLM"""
         try:
-            # Initialize DeepSeek client
-            self.deepseek_api_key = os.environ.get("DEEPSEEK_API_KEY")
-            self.deepseek_base_url = "https://api.deepseek.com/v1/chat/completions"
-            
-            # Check if API key is available
-            if not self.deepseek_api_key:
-                print("❌ DEEPSEEK_API_KEY not found in environment variables")
-                print("Please set your DeepSeek API key as a HuggingFace secret named 'DEEPSEEK_API_KEY'")
-                raise Exception("DeepSeek API key not found")
-            
-            print(f"✅ DeepSeek API key found: {self.deepseek_api_key[:20]}...")
+            # Initialize Claude client
+            self.anthropic_client = anthropic.Anthropic(
+                api_key=os.environ.get("ANTHROPIC_KEY")
+            )
             
             # Create content selection prompt
             content_selection_template = """Hi! I'm helping a student find the best curriculum slide for their question. 
-
 The student asked: "{question}"
-
 Here are some slides that might be relevant:
 {slide_contents}
-
 Could you help me pick the slide that best answers their specific question? Look for:
 - Slides that specifically mention what they're asking about
 - Slides with clear explanations and examples
 - Slides that match the exact terms they used (like "for loops" vs just "loops")
-
 Just respond with the slide number (1, 2, 3, etc.) that you think is most helpful. If none really fit, say "0".
-
 Thanks! Slide number:"""
             
             self.content_selection_prompt = PromptTemplate(
@@ -115,12 +243,9 @@ Thanks! Slide number:"""
             
             # Create answer generation prompt
             answer_template = """Hey there! I'm helping a student understand a programming concept. They asked:
-
 "{question}"
-
 Here's what the curriculum slide says about it:
 {slide_content}
-
 Could you help me explain this to them in a friendly, educational way? I'd like you to:
 - Break it down in simple terms
 - Use examples if the slide has them
@@ -128,7 +253,6 @@ Could you help me explain this to them in a friendly, educational way? I'd like
 - Add some helpful context if the slide is brief
 - Use bullet points or lists to make it clear
 - Make sure your answer directly addresses what they asked
-
 Thanks for your help! Here's what I'd tell the student:"""
             
             self.answer_prompt = PromptTemplate(
@@ -140,7 +264,7 @@ Thanks for your help! Here's what I'd tell the student:"""
             
         except Exception as e:
             print(f"❌ Error setting up LLM: {e}")
-            self.deepseek_api_key = None
+            self.anthropic_client = None
             self.content_selection_prompt = None
             self.answer_prompt = None
 
@@ -180,7 +304,7 @@ Thanks for your help! Here's what I'd tell the student:"""
         selected_content = None
         selected_result = None
         
-        if self.deepseek_api_key and self.content_selection_prompt:
+        if self.anthropic_client and self.content_selection_prompt:
             try:
                 # Prepare slide contents for LLM analysis
                 slide_contents = []
@@ -192,7 +316,7 @@ Thanks for your help! Here's what I'd tell the student:"""
                 
                 slide_contents_text = "\n\n".join(slide_contents)
                 
-                print("🤖 Using DeepSeek to select most relevant content...")
+                print("🤖 Using LLM to select most relevant content...")
                 
                 # Format the prompt
                 prompt = self.content_selection_prompt.format(
@@ -200,32 +324,16 @@ Thanks for your help! Here's what I'd tell the student:"""
                     slide_contents=slide_contents_text
                 )
                 
-                # Get DeepSeek's selection
-                headers = {
-                    "Authorization": f"Bearer {self.deepseek_api_key}",
-                    "Content-Type": "application/json"
-                }
-                
-                data = {
-                    "model": "deepseek-chat",
-                    "messages": [{"role": "user", "content": prompt}],
-                    "max_tokens": 1500,
-                    "temperature": 0.7
-                }
-                
-                response = requests.post(self.deepseek_base_url, headers=headers, json=data)
-                
-                if response.status_code == 401:
-                    print("❌ DeepSeek API key is invalid or expired")
-                    print("Please check your DeepSeek API key in HuggingFace secrets")
-                    raise Exception("Invalid DeepSeek API key")
-                elif response.status_code != 200:
-                    print(f"❌ DeepSeek API error: {response.status_code} - {response.text}")
-                    raise Exception(f"DeepSeek API error: {response.status_code}")
+                # Get LLM's selection
+                response = self.anthropic_client.messages.create(
+                    model="claude-3-5-haiku-20241022",
+                    max_tokens=1500,
+                    temperature=0.7,
+                    messages=[{"role": "user", "content": prompt}]
+                )
                 
-                response.raise_for_status()
-                selection_response = response.json()["choices"][0]["message"]["content"]
-                print(f"DeepSeek Selection Response: {selection_response}")
+                selection_response = response.content[0].text
+                print(f"LLM Selection Response: {selection_response}")
                 
                 # Parse the selection
                 try:
@@ -261,9 +369,9 @@ Thanks for your help! Here's what I'd tell the student:"""
         
         # Step 3: LLM answer generation
         answer = ""
-        if self.deepseek_api_key and self.answer_prompt and selected_content:
+        if self.anthropic_client and self.answer_prompt and selected_content:
             try:
-                print("🤖 Generating DeepSeek answer...")
+                print("🤖 Generating LLM answer...")
                 
                 # Format the prompt
                 prompt = self.answer_prompt.format(
@@ -271,35 +379,19 @@ Thanks for your help! Here's what I'd tell the student:"""
                     slide_content=selected_content
                 )
                 
-                # Get DeepSeek's answer
-                headers = {
-                    "Authorization": f"Bearer {self.deepseek_api_key}",
-                    "Content-Type": "application/json"
-                }
-                
-                data = {
-                    "model": "deepseek-chat",
-                    "messages": [{"role": "user", "content": prompt}],
-                    "max_tokens": 1500,
-                    "temperature": 0.7
-                }
-                
-                response = requests.post(self.deepseek_base_url, headers=headers, json=data)
-                
-                if response.status_code == 401:
-                    print("❌ DeepSeek API key is invalid or expired")
-                    print("Please check your DeepSeek API key in HuggingFace secrets")
-                    raise Exception("Invalid DeepSeek API key")
-                elif response.status_code != 200:
-                    print(f"❌ DeepSeek API error: {response.status_code} - {response.text}")
-                    raise Exception(f"DeepSeek API error: {response.status_code}")
+                # Get LLM's answer
+                response = self.anthropic_client.messages.create(
+                    model="claude-3-5-haiku-20241022",
+                    max_tokens=1500,
+                    temperature=0.7,
+                    messages=[{"role": "user", "content": prompt}]
+                )
                 
-                response.raise_for_status()
-                answer = response.json()["choices"][0]["message"]["content"].strip()
-                print(f"✅ DeepSeek answer generated: {answer[:100]}...")
+                answer = response.content[0].text.strip()
+                print(f"✅ LLM answer generated: {answer[:100]}...")
                 
             except Exception as e:
-                print(f"Error generating DeepSeek answer: {e}")
+                print(f"Error generating LLM answer: {e}")
                 answer = f"Based on the curriculum slide:\n\n{selected_content}\n\nThis slide contains relevant information about your question."
         else:
             answer = f"Based on the curriculum slide:\n\n{selected_content}\n\nThis slide contains relevant information about your question."
@@ -342,44 +434,128 @@ Thanks for your help! Here's what I'd tell the student:"""
 
 # --- Gradio UI ---
 assistant = LLMCurriculumAssistant()
+practice_assistant = CodePracticeAssistant()
 
 def gradio_chat(query):
     """Gradio chat interface"""
     answer, relevant_slides, recommended_slide, recommended_label = assistant.chat(query)
     return answer, relevant_slides
 
+def generate_problem(topic, problem_type):
+    """Generate a practice problem"""
+    problem, code = practice_assistant.generate_practice_problem(topic, problem_type)
+    return problem, code
+
+def analyze_code(topic, problem_type, problem_description, student_code):
+    """Analyze student's code"""
+    analysis = practice_assistant.analyze_student_code(topic, problem_type, problem_description, student_code)
+    return analysis
+
 with gr.Blocks(title="LLM Curriculum Assistant", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🤖 LLM Curriculum Assistant\nYour AI programming tutor with LLM-powered content selection and answers!")
+    gr.Markdown("# 🤖 LLM Curriculum Assistant\nYour AI programming tutor with LLM-powered content selection and code practice!")
     
-    with gr.Row():
-        # Left Column - Chatbot Interface
-        with gr.Column(scale=1):
-            gr.Markdown("### 💬 Chatbot")
-            gr.Markdown("**Ask questions about programming concepts:**")
+    with gr.Tabs():
+        # Tab 1: Chat Assistant
+        with gr.Tab("💬 Chat Assistant"):
+            with gr.Row():
+                # Left Column - Chatbot Interface
+                with gr.Column(scale=1):
+                    gr.Markdown("### 💬 Chatbot")
+                    gr.Markdown("**Ask questions about programming concepts:**")
+                    
+                    question = gr.Textbox(
+                        label="Question Input", 
+                        placeholder="e.g., What are for loops? How do variables work? Explain functions...", 
+                        lines=3
+                    )
+                    submit = gr.Button("🤖 Ask AI", variant="primary", size="lg")
+                    answer = gr.Markdown(label="LLM Generated Answer")
+                
+                # Right Column - Slides Display
+                with gr.Column(scale=1):
+                    gr.Markdown("### 📄 Most Relevant Slides")
+                    gallery = gr.Gallery(
+                        label="Curriculum Slides", 
+                        columns=1, 
+                        rows=3, 
+                        height="600px", 
+                        object_fit="contain",
+                        show_label=False
+                    )
             
-            question = gr.Textbox(
-                label="Question Input", 
-                placeholder="e.g., What are for loops? How do variables work? Explain functions...", 
-                lines=3
-            )
-            submit = gr.Button("🤖 Ask AI", variant="primary", size="lg")
-            answer = gr.Markdown(label="LLM Generated Answer")
+            # Event handlers for chat
+            submit.click(fn=gradio_chat, inputs=[question], outputs=[answer, gallery])
+            question.submit(fn=gradio_chat, inputs=[question], outputs=[answer, gallery])
         
-        # Right Column - Slides Display
-        with gr.Column(scale=1):
-            gr.Markdown("### 📄 Most Relevant Slides")
-            gallery = gr.Gallery(
-                label="Curriculum Slides", 
-                columns=1, 
-                rows=3, 
-                height="600px", 
-                object_fit="contain",
-                show_label=False
+        # Tab 2: Code Practice
+        with gr.Tab("💻 Code Practice"):
+            gr.Markdown("### 🎯 Practice Programming Skills")
+            gr.Markdown("Choose a topic and problem type to get started!")
+            
+            with gr.Row():
+                # Left Column - Problem Setup
+                with gr.Column(scale=1):
+                    gr.Markdown("#### 📝 Problem Setup")
+                    
+                    topic_input = gr.Textbox(
+                        label="Topic to Practice",
+                        placeholder="e.g., for loops, functions, variables, arrays, recursion...",
+                        lines=2
+                    )
+                    
+                    problem_type = gr.Dropdown(
+                        label="Problem Type",
+                        choices=[
+                            "Create Practice Problems",
+                            "Debug - Identify Error Type",
+                            "Debug - Explain Error Reason", 
+                            "Debug - Fix the Error",
+                            "Optimize Code Performance"
+                        ],
+                        value="Create Practice Problems"
+                    )
+                    
+                    generate_btn = gr.Button("🎲 Generate Problem", variant="primary", size="lg")
+                    
+                    gr.Markdown("#### 📋 Problem Description")
+                    problem_description = gr.Markdown(label="Problem will appear here...")
+                    
+                    gr.Markdown("#### 💻 Starter Code (if applicable)")
+                    starter_code = gr.Code(
+                        label="Code Editor",
+                        language="python",
+                        lines=10,
+                        placeholder="# Write your code here..."
+                    )
+                
+                # Right Column - Student Work & Analysis
+                with gr.Column(scale=1):
+                    gr.Markdown("#### ✍️ Your Solution")
+                    
+                    student_code = gr.Code(
+                        label="Your Code",
+                        language="python",
+                        lines=15,
+                        placeholder="# Write your solution here..."
+                    )
+                    
+                    analyze_btn = gr.Button("🔍 Analyze My Code", variant="secondary", size="lg")
+                    
+                    gr.Markdown("#### 📊 AI Analysis")
+                    analysis_output = gr.Markdown(label="Analysis will appear here...")
+            
+            # Event handlers for practice
+            generate_btn.click(
+                fn=generate_problem,
+                inputs=[topic_input, problem_type],
+                outputs=[problem_description, starter_code]
+            )
+            
+            analyze_btn.click(
+                fn=analyze_code,
+                inputs=[topic_input, problem_type, problem_description, student_code],
+                outputs=[analysis_output]
             )
-    
-    # Event handlers
-    submit.click(fn=gradio_chat, inputs=[question], outputs=[answer, gallery])
-    question.submit(fn=gradio_chat, inputs=[question], outputs=[answer, gallery])
 
 if __name__ == "__main__":
     demo.launch()