Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 from langchain.prompts import ChatPromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
 from langchain.schema import HumanMessage, SystemMessage
-from langchain.callbacks.base import BaseCallbackHandler
 import os
 import time
 import logging
@@ -115,7 +114,7 @@ def smart_truncate(text, max_length=3000):
 
 def respond_with_enhanced_streaming(
     message,
-    history: list[tuple[str, str]],
+    history,
     system_message,
     max_tokens,
     temperature,
@@ -155,7 +154,7 @@ def respond_with_enhanced_streaming(
             # Update every 4 words for smooth streaming effect
             if i % 4 == 0:
                 yield partial_response
-                time.sleep(0.03)  # Slightly faster streaming
+                time.sleep(0.03)
         
         # Final complete response
         final_response = f"*{mode}*\n\n{response}"
@@ -166,7 +165,7 @@ def respond_with_enhanced_streaming(
         logger.exception("Error in LangChain response generation")
         yield f"Sorry, I encountered an error: {str(e)[:150]}"
 
-# Create enhanced Gradio interface (simplified for compatibility)
+# Create enhanced Gradio interface
 demo = gr.ChatInterface(
     respond_with_enhanced_streaming,
     title="🎓 EduBot | AI Learning Assistant",
@@ -182,316 +181,4 @@ demo = gr.ChatInterface(
     📚 **Educational Modes:**
     • 🧮 **Math Mode** - Step-by-step problem solving with detailed explanations
     • 🔍 **Research Mode** - Source finding, evaluation, and citation guidance
-    • 📚 **Study Mode** - Learning strategies and exam preparation techniques
-    • 🎓 **General Mode** - Comprehensive educational support
-    
-    💡 **Tip:** Try asking detailed questions for thorough explanations!
-    """,
-    examples=[
-        ["Solve the quadratic equation x² + 5x + 6 = 0 with complete step-by-step explanations"],
-        ["How do I conduct a comprehensive literature review for my psychology research paper?"],
-        ["Create a detailed study schedule for my calculus and chemistry final exams"],
-        ["Explain derivatives in calculus with real-world applications and examples"],
-        ["How do I properly format citations in APA style with detailed guidelines?"]
-    ],
-    additional_inputs=[
-        gr.Textbox(
-            value="You are EduBot, an expert AI learning assistant. Provide comprehensive, educational responses that help students truly understand concepts.",
-            label="Custom System Message",
-            placeholder="Customize how EduBot behaves...",
-            lines=2
-        ),
-        gr.Slider(
-            minimum=1, 
-            maximum=1024, 
-            value=600, 
-            step=1, 
-            label="Max Tokens"
-        ),
-        gr.Slider(
-            minimum=0.1, 
-            maximum=2.0, 
-            value=0.7, 
-            step=0.1, 
-            label="Temperature"
-        ),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.9,
-            step=0.05,
-            label="Top-p"
-        ),
-    ],
-    theme=gr.themes.Soft(
-        primary_hue="blue",
-        secondary_hue="green"
-    )
-)
-
-if __name__ == "__main__":
-    logger.info("Starting EduBot application...")
-    demo.launch()    def on_llm_end(self, response, **kwargs):
-        """Called when LLM finishes generating"""
-        self.is_streaming = False
-        logger.info(f"LLM generation completed. Total tokens: {len(self.tokens)}")
-    
-    def on_llm_error(self, error, **kwargs):
-        """Called when LLM encounters an error"""
-        self.is_streaming = False
-        logger.error(f"LLM error: {error}")
-        self.text += f"\n[Error: {str(error)[:100]}]"
-
-# Set up LangChain model with conservative settings
-llm = HuggingFaceEndpoint(
-    repo_id="HuggingFaceH4/zephyr-7b-beta",
-    temperature=0.7,
-    top_p=0.9,
-    repetition_penalty=1.1,
-    model_kwargs={"max_length": 1024},  # More conservative
-    huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN")
-)
-
-# Enhanced prompt templates that use system_message parameter
-math_template = ChatPromptTemplate.from_messages([
-    ("system", """{system_message}
-
-You are an expert math tutor. For every math problem:
-1. Break it down step-by-step with detailed explanations
-2. Explain the reasoning behind each step thoroughly
-3. Show all work clearly with proper mathematical notation
-4. Check your answer and explain why it's correct
-5. Provide additional examples if helpful
-6. Explain the underlying mathematical concepts
-
-Be comprehensive and educational. Structure your response clearly."""),
-    ("human", "{question}")
-])
-
-research_template = ChatPromptTemplate.from_messages([
-    ("system", """{system_message}
-
-You are a research skills mentor. Help students with:
-- Finding reliable and credible sources
-- Evaluating source credibility and bias
-- Proper citation formats (APA, MLA, Chicago, etc.)
-- Research strategies and methodologies
-- Academic writing techniques and structure
-- Database navigation and search strategies
-
-Provide detailed, actionable advice with specific examples."""),
-    ("human", "{question}")
-])
-
-study_template = ChatPromptTemplate.from_messages([
-    ("system", """{system_message}
-
-You are a study skills coach. Help students with:
-- Effective study methods for different learning styles
-- Time management and scheduling techniques
-- Memory techniques and retention strategies
-- Test preparation and exam strategies
-- Note-taking methods and organization
-- Learning style optimization
-
-Provide comprehensive, personalized advice with practical examples."""),
-    ("human", "{question}")
-])
-
-general_template = ChatPromptTemplate.from_messages([
-    ("system", """{system_message}
-
-You are EduBot, a comprehensive AI learning assistant. You help students with:
-📐 Mathematics (detailed step-by-step solutions and concept explanations)
-🔍 Research skills (source finding, evaluation, and citation)
-📚 Study strategies (effective learning techniques and exam preparation)
-🛠️ Educational tools (guidance on learning resources and technologies)
-
-Always be encouraging, patient, thorough, and comprehensive."""),
-    ("human", "{question}")
-])
-
-def detect_subject(message):
-    """Determine which prompt template to use based on the message"""
-    message_lower = message.lower()
-    
-    math_keywords = ['math', 'solve', 'calculate', 'equation', 'formula', 'algebra', 'geometry', 'calculus', 'derivative', 'integral', 'theorem', 'proof']
-    research_keywords = ['research', 'source', 'citation', 'bibliography', 'reference', 'academic', 'paper', 'essay', 'thesis', 'database', 'journal']
-    study_keywords = ['study', 'memorize', 'exam', 'test', 'quiz', 'review', 'learn', 'remember', 'focus', 'motivation', 'notes']
-    
-    if any(keyword in message_lower for keyword in math_keywords):
-        return math_template, "🧮 Math Mode"
-    elif any(keyword in message_lower for keyword in research_keywords):
-        return research_template, "🔍 Research Mode"
-    elif any(keyword in message_lower for keyword in study_keywords):
-        return study_template, "📚 Study Mode"
-    else:
-        return general_template, "🎓 General Mode"
-
-def smart_truncate(text, max_length=3000):
-    """Intelligently truncate text at sentence boundaries"""
-    if len(text) <= max_length:
-        return text
-    
-    # Try to cut at last complete sentence
-    sentences = re.split(r'(?<=[.!?]) +', text[:max_length])
-    if len(sentences) > 1:
-        # Remove the last incomplete sentence
-        return ' '.join(sentences[:-1]) + "... [Response truncated - ask for continuation]"
-    else:
-        # Fallback to word boundary
-        words = text[:max_length].split()
-        return ' '.join(words[:-1]) + "... [Response truncated - ask for continuation]"
-
-def respond_with_enhanced_streaming(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    """Enhanced LangChain implementation with proper system message handling"""
-    
-    try:
-        # Select template and get mode
-        template, mode = detect_subject(message)
-        
-        # Create LangChain chain
-        chain = template | llm
-        
-        # Show initial mode
-        yield f"*{mode}*\n\nGenerating response..."
-        
-        # Get complete response from LangChain with system message
-        logger.info(f"Processing {mode} query: {message[:50]}...")
-        
-        response = chain.invoke({
-            "question": message,
-            "system_message": system_message or "You are EduBot, an AI learning assistant."
-        })
-        
-        # Smart truncation at sentence boundaries
-        response = smart_truncate(response, max_length=3000)
-        
-        # Simulate streaming by chunking the response
-        words = response.split()
-        partial_response = f"*{mode}*\n\n"
-        
-        # Stream word by word for better UX
-        for i, word in enumerate(words):
-            partial_response += word + " "
-            
-            # Update every 4 words for smooth streaming effect
-            if i % 4 == 0:
-                yield partial_response
-                time.sleep(0.03)  # Slightly faster streaming
-        
-        # Final complete response
-        final_response = f"*{mode}*\n\n{response}"
-        logger.info(f"Response completed. Length: {len(response)} characters")
-        yield final_response
-        
-    except Exception as e:
-        logger.exception("Error in LangChain response generation")
-        yield f"Sorry, I encountered an error: {str(e)[:150]}"
-
-# Create enhanced Gradio interface with custom theme
-custom_theme = gr.themes.Soft(
-    primary_hue="blue",
-    secondary_hue="green",
-    neutral_hue="slate",
-).set(
-    body_background_fill="linear-gradient(45deg, #f0f9ff, #ecfdf5)",
-    button_primary_background_fill="#2563eb",
-    button_primary_text_color="white",
-)
-
-# Create the main interface
-demo = gr.ChatInterface(
-    respond_with_enhanced_streaming,
-    title="🎓 EduBot | AI Learning Assistant",
-    description="""
-    **Your comprehensive AI tutor powered by LangChain!**
-    
-    🔧 **Technical Features:**
-    • Dynamic prompt templates based on question type
-    • LangChain chain composition with `|` operator  
-    • Smart response truncation at sentence boundaries
-    • Enhanced error handling and logging
-    
-    📚 **Educational Modes:**
-    • 🧮 **Math Mode** - Step-by-step problem solving with detailed explanations
-    • 🔍 **Research Mode** - Source finding, evaluation, and citation guidance
-    • 📚 **Study Mode** - Learning strategies and exam preparation techniques
-    • 🎓 **General Mode** - Comprehensive educational support
-    
-    💡 **Tip:** Try asking detailed questions for thorough explanations!
-    """,
-    examples=[
-        ["Solve the quadratic equation x² + 5x + 6 = 0 with complete step-by-step explanations"],
-        ["How do I conduct a comprehensive literature review for my psychology research paper?"],
-        ["Create a detailed study schedule for my calculus and chemistry final exams"],
-        ["Explain derivatives in calculus with real-world applications and examples"],
-        ["How do I properly format citations in APA style with detailed guidelines?"]
-    ],
-    additional_inputs=[
-        gr.Textbox(
-            value="You are EduBot, an expert AI learning assistant. Provide comprehensive, educational responses that help students truly understand concepts.",
-            label="Custom System Message",
-            placeholder="Customize how EduBot behaves...",
-            lines=2,
-            visible=True  # Make it visible so users can customize
-        ),
-        gr.Slider(
-            minimum=1, 
-            maximum=1024, 
-            value=600, 
-            step=1, 
-            label="Max Tokens",
-            info="Higher values = longer responses"
-        ),
-        gr.Slider(
-            minimum=0.1, 
-            maximum=2.0, 
-            value=0.7, 
-            step=0.1, 
-            label="Temperature",
-            info="Higher values = more creative responses"
-        ),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.9,
-            step=0.05,
-            label="Top-p",
-            info="Controls response diversity"
-        ),
-    ],
-    theme=custom_theme,
-    chatbot=gr.Chatbot(
-        height=500,
-        show_copy_button=True,
-        avatar_images=["👤", "🎓"],
-        bubble_full_width=False,
-        show_share_button=True,
-        placeholder="Hi! I'm EduBot. What would you like to learn today? 📚"
-    ),
-    textbox=gr.Textbox(
-        placeholder="Ask me about math, research, study strategies, or any educational topic...",
-        container=False,
-        scale=7
-    ),
-    retry_btn="🔄 Retry",
-    undo_btn="↩️ Undo",
-    clear_btn="🗑️ Clear",
-)
-
-if __name__ == "__main__":
-    logger.info("Starting EduBot application...")
-    demo.launch(
-        share=False,  # Set to True if you want a public link
-        show_error=True,
-        favicon_path=None,
-        show_api=False
-    )
+    • 📚 **Study Mode** - Learning strategies and exam preparation techniques