app.py

"""
Gradio web interface for the TutorX MCP Server with SSE support
"""

import os
import gradio as gr
import numpy as np
import json

from datetime import datetime
import asyncio
import aiohttp
import sseclient
import requests

# Import MCP SSE client context managers
from mcp import ClientSession
from mcp.client.sse import sse_client

# Server configuration
SERVER_URL = "http://localhost:8000/sse"  # Ensure this is the SSE endpoint

# Utility functions


async def load_concept_graph(concept_id: str = None):
    """
    Load and visualize the concept graph for a given concept ID.
    If no concept_id is provided, returns the first available concept.
    Uses call_resource for concept graph retrieval (not a tool).
    
    Returns:
        tuple: (figure, concept_details, related_concepts) or (None, error_dict, [])
    """
    try:
        print(f"[DEBUG] Loading concept graph for concept_id: {concept_id}")
        async with sse_client(SERVER_URL) as (sse, write):
            async with ClientSession(sse, write) as session:
                await session.initialize()
                # Use MCP resource call for concept graph
                result = await session.call_resource("resources/read", {"uri": f"concept-graph://{concept_id}" if concept_id else "concept-graph://"})
                print(f"[DEBUG] Server response: {result}")
                if not result or not isinstance(result, dict):
                    error_msg = "Invalid server response"
                    print(f"[ERROR] {error_msg}")
                    return None, {"error": error_msg}, []
                if "error" in result:
                    print(f"[ERROR] Server returned error: {result['error']}")
                    return None, {"error": result["error"]}, []
                if "concepts" in result and not concept_id:
                    if not result["concepts"]:
                        error_msg = "No concepts available"
                        print(f"[ERROR] {error_msg}")
                        return None, {"error": error_msg}, []
                    concept = result["concepts"][0]
                    print(f"[DEBUG] Using first concept from list: {concept.get('id')}")
                else:
                    concept = result.get("concept", result)
                    print(f"[DEBUG] Using direct concept: {concept.get('id')}")
                if not isinstance(concept, dict) or not concept.get('id'):
                    error_msg = "Invalid concept data structure"
                    print(f"[ERROR] {error_msg}: {concept}")
                    return None, {"error": error_msg}, []
                import matplotlib.pyplot as plt
                import networkx as nx
                G = nx.DiGraph()
                G.add_node(concept["id"], label=concept["name"], type="concept")
                related_concepts = []
                if "related" in concept:
                    for rel_id in concept["related"]:
                        rel_result = await session.call_tool("get_concept_graph", {"concept_id": rel_id})
                        if "error" not in rel_result:
                            G.add_node(rel_id, label=rel_result["name"], type="related")
                            G.add_edge(concept["id"], rel_id, relationship="related_to")
                            related_concepts.append([rel_id, rel_result.get("name", ""), rel_result.get("description", "")])
                if "prerequisites" in concept:
                    for prereq_id in concept["prerequisites"]:
                        prereq_result = await session.call_tool("get_concept_graph", {"concept_id": prereq_id})
                        if "error" not in prereq_result:
                            G.add_node(prereq_id, label=prereq_result["name"], type="prerequisite")
                            G.add_edge(prereq_id, concept["id"], relationship="prerequisite_for")
                plt.figure(figsize=(10, 8))
                pos = nx.spring_layout(G)
                node_colors = []
                for node in G.nodes():
                    if G.nodes[node].get("type") == "concept":
                        node_colors.append("lightblue")
                    elif G.nodes[node].get("type") == "prerequisite":
                        node_colors.append("lightcoral")
                    else:
                        node_colors.append("lightgreen")
                nx.draw_networkx_nodes(G, pos, node_size=2000, node_color=node_colors, alpha=0.8)
                nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)
                labels = {node: G.nodes[node].get("label", node) for node in G.nodes()}
                nx.draw_networkx_labels(G, pos, labels, font_size=10, font_weight="bold")
                edge_labels = {(u, v): d["relationship"] for u, v, d in G.edges(data=True)}
                nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=8)
                plt.title(f"Concept Graph: {concept.get('name', concept_id)}")
                plt.axis("off")
                concept_details = {
                    "id": concept.get("id", ""),
                    "name": concept.get("name", ""),
                    "description": concept.get("description", ""),
                    "related_concepts_count": len(concept.get("related", [])),
                    "prerequisites_count": len(concept.get("prerequisites", []))
                }
                return plt.gcf(), concept_details, related_concepts
    except Exception as e:
        import traceback
        traceback.print_exc()
        return None, {"error": f"Failed to load concept graph: {str(e)}"}, []
        
# Create Gradio interface
with gr.Blocks(title="TutorX Educational AI", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 📚 TutorX Educational AI Platform")
    gr.Markdown("""
    An adaptive, multi-modal, and collaborative AI tutoring platform built with MCP.
    
    This interface demonstrates the functionality of the TutorX MCP server using SSE connections.
    """)
    
    # Set a default student ID for the demo
    student_id = "student_12345"
    
    with gr.Tabs() as tabs:
        # Tab 1: Core Features
        with gr.Tab("Core Features"):
            with gr.Blocks() as concept_graph_tab:
                gr.Markdown("## Concept Graph Visualization")
                with gr.Row():
                    with gr.Column(scale=3):
                        concept_id = gr.Dropdown(
                            label="Select a Concept",
                            choices=["python", "functions", "oop", "data_structures"],
                            value="python",
                            interactive=True
                        )
                        load_concept_btn = gr.Button("Load Concept Graph", variant="primary")
                        
                        # Concept details
                        concept_details = gr.JSON(label="Concept Details")
                        
                        # Related concepts
                        related_concepts = gr.Dataframe(
                            headers=["ID", "Name", "Description"],
                            datatype=["str", "str", "str"],
                            label="Related Concepts"
                        )
                    
                    # Graph visualization
                    with gr.Column(scale=7):
                        graph_output = gr.Plot(label="Concept Graph")
                
                # Button click handler
                load_concept_btn.click(
                    fn=load_concept_graph,
                    inputs=[concept_id],
                    outputs=[graph_output, concept_details, related_concepts]
                )
                
                # Load default concept on tab click
                concept_graph_tab.load(
                    fn=load_concept_graph,
                    inputs=[concept_id],
                    outputs=[graph_output, concept_details, related_concepts]
                )
            
            gr.Markdown("## Assessment Generation")
            with gr.Row():
                with gr.Column():
                    concept_input = gr.Textbox(
                        label="Enter Concept",
                        placeholder="e.g., Linear Equations, Photosynthesis, World War II",
                        lines=2
                    )
                    with gr.Row():
                        diff_input = gr.Slider(
                            minimum=1, 
                            maximum=5, 
                            value=2, 
                            step=1, 
                            label="Difficulty Level",
                            interactive=True
                        )
                        gen_quiz_btn = gr.Button("Generate Quiz", variant="primary")
                
                with gr.Column():
                    quiz_output = gr.JSON(label="Generated Quiz")
            
            async def on_generate_quiz(concept, difficulty):
                try:
                    if not concept or not str(concept).strip():
                        return {"error": "Please enter a concept"}
                    try:
                        difficulty = int(float(difficulty))
                        difficulty = max(1, min(5, difficulty))
                    except (ValueError, TypeError):
                        difficulty = 3
                    # Map numeric difficulty to string
                    if difficulty <= 2:
                        difficulty_str = "easy"
                    elif difficulty == 3:
                        difficulty_str = "medium"
                    else:
                        difficulty_str = "hard"
                    async with sse_client(SERVER_URL) as (sse, write):
                        async with ClientSession(sse, write) as session:
                            await session.initialize()
                            response = await session.call_tool("generate_quiz_tool", {"concept": concept.strip(), "difficulty": difficulty_str})
                            return response
                except Exception as e:
                    import traceback
                    return {
                        "error": f"Error generating quiz: {str(e)}\n\n{traceback.format_exc()}"
                    }
                
            gen_quiz_btn.click(
                fn=on_generate_quiz,
                inputs=[concept_input, diff_input],
                outputs=[quiz_output],
                api_name="generate_quiz"
            )
        
        # Tab 2: Advanced Features
        with gr.Tab("Advanced Features"):
            gr.Markdown("## Lesson Generation")
            
            with gr.Row():
                with gr.Column():
                    topic_input = gr.Textbox(label="Lesson Topic", value="Solving Quadratic Equations")
                    grade_input = gr.Slider(minimum=1, maximum=12, value=9, step=1, label="Grade Level")
                    duration_input = gr.Slider(minimum=15, maximum=90, value=45, step=5, label="Duration (minutes)")
                    gen_lesson_btn = gr.Button("Generate Lesson Plan")
                
                with gr.Column():
                    lesson_output = gr.JSON(label="Lesson Plan")
            async def generate_lesson_async(topic, grade, duration):
                async with sse_client(SERVER_URL) as (sse, write):
                    async with ClientSession(sse, write) as session:
                        await session.initialize()
                        response = await session.call_tool("generate_lesson_tool", {"topic": topic, "grade_level": grade, "duration_minutes": duration})
                        return response
                
            gen_lesson_btn.click(
                fn=generate_lesson_async,
                inputs=[topic_input, grade_input, duration_input],
                outputs=[lesson_output]
            )
            
            gr.Markdown("## Curriculum Standards")
            
            with gr.Row():
                with gr.Column():
                    country_input = gr.Dropdown(
                        choices=["US", "UK"],
                        label="Country",
                        value="US"
                    )
                    standards_btn = gr.Button("Get Standards")
                
                with gr.Column():
                    standards_output = gr.JSON(label="Curriculum Standards")
            
            async def get_standards_async(country):
                try:
                    # Convert display text to lowercase for the API
                    country_code = country.lower()
                    async with sse_client(SERVER_URL) as (sse, write):
                        async with ClientSession(sse, write) as session:
                            await session.initialize()
                            response = await session.call_tool("get_curriculum_standards", {"country_code": country_code})
                            
                            # Format the response for better display
                            if "standards" in response:
                                formatted = {
                                    "country": response["standards"]["name"],
                                    "subjects": {},
                                    "website": response["standards"].get("website", "")
                                }
                                
                                # Format subjects and domains
                                for subj_key, subj_info in response["standards"]["subjects"].items():
                                    formatted["subjects"][subj_key] = {
                                        "description": subj_info["description"],
                                        "domains": subj_info["domains"]
                                    }
                                
                                # Add grade levels or key stages if available
                                if "grade_levels" in response["standards"]:
                                    formatted["grade_levels"] = response["standards"]["grade_levels"]
                                elif "key_stages" in response["standards"]:
                                    formatted["key_stages"] = response["standards"]["key_stages"]
                                    
                                return formatted
                            return response
                except Exception as e:
                    return {"error": f"Failed to fetch standards: {str(e)}"}
                
            standards_btn.click(
                fn=get_standards_async,
                inputs=[country_input],
                outputs=[standards_output]
            )
        
        # Tab 3: Multi-Modal Interaction
        with gr.Tab("Multi-Modal Interaction"):
            gr.Markdown("## Text Interaction")
            
            with gr.Row():
                with gr.Column():
                    text_input = gr.Textbox(label="Ask a Question", value="How do I solve a quadratic equation?")
                    text_btn = gr.Button("Submit")
                
                with gr.Column():
                    text_output = gr.JSON(label="Response")
            async def text_interaction_async(text):
                async with sse_client(SERVER_URL) as (sse, write):
                    async with ClientSession(sse, write) as session:
                        await session.initialize()
                        response = await session.call_tool("text_interaction", {"query": text, "student_id": student_id})
                        return response
                
            text_btn.click(
                fn=text_interaction_async,
                inputs=[text_input],
                outputs=[text_output]
            )
            
            gr.Markdown("## PDF OCR and Summarization (Coming Soon)")
            with gr.Row():
                with gr.Column():
                    pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
                    ocr_btn = gr.Button("Extract Text")
                
                with gr.Column():
                    summary_output = gr.JSON(label="Summary")
            
            async def pdf_ocr_async(pdf_file):
                if not pdf_file:
                    return {"error": "No PDF file provided", "success": False}
                try:
                    # Get the file path from the Gradio file object
                    if isinstance(pdf_file, dict):
                        file_path = pdf_file.get("path", "")
                    else:
                        file_path = pdf_file
                    
                    if not file_path or not os.path.exists(file_path):
                        return {"error": "File not found", "success": False}
                        
                    async with sse_client(SERVER_URL) as (sse, write):
                        async with ClientSession(sse, write) as session:
                            await session.initialize()
                            response = await session.call_tool("pdf_ocr", {"pdf_file": file_path})
                            return response
                except Exception as e:
                    return {"error": f"Error processing PDF: {str(e)}", "success": False}
                
            ocr_btn.click(
                fn=pdf_ocr_async,
                inputs=[pdf_input],
                outputs=[summary_output]
            )
        
        # Tab 4: Analytics
        with gr.Tab("Analytics"):
            gr.Markdown("## Plagiarism Detection")
            
            with gr.Row():
                with gr.Column():
                    submission_input = gr.Textbox(
                        label="Student Submission",
                        lines=5,
                        value="The quadratic formula states that if ax² + bx + c = 0, then x = (-b ± √(b² - 4ac)) / 2a."
                    )
                    reference_input = gr.Textbox(
                        label="Reference Source",
                        lines=5,
                        value="According to the quadratic formula, for any equation in the form ax² + bx + c = 0, the solutions are x = (-b ± √(b² - 4ac)) / 2a."
                    )
                    plagiarism_btn = gr.Button("Check Originality")
                
                with gr.Column():
                    plagiarism_output = gr.JSON(label="Originality Report")
            
            async def check_plagiarism_async(submission, reference):
                async with sse_client(SERVER_URL) as (sse, write):
                    async with ClientSession(sse, write) as session:
                        await session.initialize()
                        response = await session.call_tool("check_submission_originality", {"submission": submission, "reference_sources": reference})
                        return response
                
            plagiarism_btn.click(
                fn=check_plagiarism_async,
                inputs=[submission_input, reference_input],
                outputs=[plagiarism_output]
            )

# Launch the interface
if __name__ == "__main__":
    demo.queue().launch(server_name="0.0.0.0", server_port=7860)