app.py

import gradio as gr
import pandas as pd
import numpy as np
import random
import time
import concurrent.futures
import matplotlib.pyplot as plt

from typing import Dict, Any, List, Union
from sympy import symbols, Eq, solve
from sympy.parsing.sympy_parser import parse_expr
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from fenics import (
    Mesh,
    FunctionSpace,
    TrialFunction,
    TestFunction,
    dot,
    grad,
    dx,
    solve,
    Function,
    Constant,
    DirichletBC
)
  # Ensure FEniCS is installed

########################################
# 1. Domain Assumption Matrix
########################################

class DomainAssumptionMatrix:
    """
    Stores domain assumptions (e.g., PDE dimension=2D/3D),
    checks for conflicts across domains if needed.
    """
    def __init__(self):
        self.matrix: Dict[str, Dict[str, Any]] = {}

    def add_domain(self, domain_name: str, assumptions: Dict[str, Any]) -> str:
        """
        Add or update domain_name with given assumptions.
        Example: domain='NavierStokes', assumptions={'dimension': '3D', 'time_dependent': True}
        """
        if domain_name not in self.matrix:
            self.matrix[domain_name] = {}
        self.matrix[domain_name].update(assumptions)
        return f"Domain '{domain_name}' updated with {assumptions}"

    def check_conflict(self, domain1: str, domain2: str) -> bool:
        """
        Return True if domain1 & domain2 have conflicting assumption keys.
        E.g. domain1['dimension']='2D' vs domain2['dimension']='3D'
        """
        d1 = self.matrix.get(domain1, {})
        d2 = self.matrix.get(domain2, {})
        for k, v in d1.items():
            if k in d2 and d2[k] != v:
                return True
        return False

    def list_domains(self) -> Dict[str, Dict[str, Any]]:
        return self.matrix

########################################
# 2. Confidence Index
########################################

class ConfidenceIndex:
    """
    Track 'conjectures' with a confidence score (0-5).
    """
    def __init__(self):
        self.index: Dict[str, Dict[str, Any]] = {}

    def add_conjecture(self, conj_id: str, score: int):
        """
        Add a new conjecture with an initial confidence score.
        """
        score_clamped = max(0, min(score, 5))
        self.index[conj_id] = {"score": score_clamped}

    def update_score(self, conj_id: str, delta: int):
        """
        Increase or decrease confidence by delta. Clamped to [0, 5].
        """
        if conj_id in self.index:
            old = self.index[conj_id]["score"]
            new_score = max(0, min(5, old + delta))
            self.index[conj_id]["score"] = new_score

    def get_score(self, conj_id: str) -> int:
        if conj_id in self.index:
            return self.index[conj_id]["score"]
        return 0

    def list_all(self) -> Dict[str, Dict[str, Any]]:
        return self.index

########################################
# 3. HPC PDE concurrency (FEniCS)
########################################

class HPCSolver:
    """
    Solves PDEs using FEniCS and generates visualizations.
    """
    def solve_pde(self, problem_type: str, mesh_size: int) -> str:
        """
        Solve a PDE using FEniCS and save a plot.
        Supported problem_type: 'Poisson'
        """
        if problem_type == "Poisson":
            try:
                mesh = UnitSquareMesh(mesh_size, mesh_size)
                V = FunctionSpace(mesh, "P", 1)

                u = TrialFunction(V)
                v = TestFunction(V)
                f = Constant(-6.0)

                a = dot(grad(u), grad(v)) * dx
                L = f * v * dx

                u_sol = Function(V)
                solve(a == L, u_sol)

                plt.figure()
                plot(u_sol, title="Poisson Solution")
                plt.colorbar()
                plt.savefig("poisson_solution.png")
                plt.close()

                return "Poisson PDE solved. Visualization saved as 'poisson_solution.png'."
            except Exception as e:
                return f"Error solving Poisson PDE: {e}"
        else:
            return "Unsupported PDE type."

    def solve_concurrent(self, tasks: List[str], sizes: List[int]) -> List[str]:
        """
        Run multiple PDE solves in parallel.
        """
        results = []
        def worker(t, sz):
            return self.solve_pde(t, sz)

        with concurrent.futures.ThreadPoolExecutor() as executor:
            futmap = {executor.submit(worker, t, s): (t, s) for t, s in zip(tasks, sizes)}
            for fut in concurrent.futures.as_completed(futmap):
                results.append(fut.result())
        return results

########################################
# 4. Theorem Prover Integration
########################################

class TheoremProver:
    """
    Integrates with a theorem prover to check proofs.
    Here, we simulate proof checking with probabilistic success.
    """
    def check_proof(self, statement: str) -> bool:
        """
        Simulate a theorem proof check.
        Returns True if proof is successful, False otherwise.
        """
        # 70% chance to pass if statement length > 10
        if len(statement) > 10:
            return random.random() < 0.7
        return False

########################################
# 5. Symbolic Solver
########################################

def symbolic_solve_equation(equation: str, vars_str: str) -> str:
    """
    Parse equation and variables, perform symbolic solve with SymPy.
    """
    varlist = [v.strip() for v in vars_str.split(",") if v.strip()]
    if not varlist:
        return "No variables provided."
    try:
        syms = symbols(varlist)
        expr = parse_expr(equation)
        eq = Eq(expr, 0)
        sol = solve(eq, syms, dict=True)
        return f"Solution: {sol}"
    except Exception as e:
        return f"Error solving symbolically: {e}"

########################################
# 6. Machine Learning Enhancements
########################################

class MLModel:
    """
    Handles training and prediction for text classification.
    """
    def __init__(self):
        self.vectorizer = None
        self.classifier = None
        self.trained = False

    def train_from_csv(self, file_obj) -> str:
        """
        Train a RandomForestClassifier from a CSV with 'text' and 'label' columns.
        """
        try:
            # Read CSV
            df = pd.read_csv(file_obj.name)
            if "text" not in df.columns or "label" not in df.columns:
                return "CSV must contain 'text' and 'label' columns."

            texts = df["text"].astype(str).tolist()
            labels = df["label"].astype(str).tolist()

            # Vectorize text
            self.vectorizer = CountVectorizer()
            X = self.vectorizer.fit_transform(texts)
            y = np.array(labels)

            # Train classifier
            self.classifier = RandomForestClassifier()
            self.classifier.fit(X, y)
            self.trained = True

            return f"Model trained on {len(texts)} samples. Labels: {set(labels)}."
        except Exception as e:
            return f"Error during training: {e}"

    def chat_response(self, user_input: str) -> str:
        """
        Generate a response based on the trained model's prediction.
        """
        if not self.trained:
            return "Model not trained. Please upload a CSV and train the model first."

        try:
            Xq = self.vectorizer.transform([user_input])
            pred = self.classifier.predict(Xq)[0]
            return f"Predicted label: {pred}"
        except Exception as e:
            return f"Error during prediction: {e}"

########################################
# 7. Unified Pipeline
########################################

class HybridAIPipeline:
    """
    Combines all functionalities into a unified pipeline.
    """
    def __init__(self):
        self.domains = DomainAssumptionMatrix()
        self.confidence = ConfidenceIndex()
        self.hpcsolver = HPCSolver()
        self.theorem = TheoremProver()
        self.ml_model = MLModel()
        # Store conjecture text for theorem checks
        self.conjecture_texts: Dict[str, str] = {}

    # Domain Management
    def add_domain(self, domain_name: str, key: str, val: str) -> str:
        return self.domains.add_domain(domain_name, {key: val})

    def list_domains(self) -> str:
        domains = self.domains.list_domains()
        return "\n".join([f"{k}: {v}" for k, v in domains.items()])

    # Conjecture Management
    def add_conjecture(self, conj_id: str, score: int, text: str = "") -> str:
        self.confidence.add_conjecture(conj_id, score)
        if text:
            self.conjecture_texts[conj_id] = text
        return f"Conjecture '{conj_id}' added with initial score {score}."

    def list_conjectures(self) -> str:
        conjectures = self.confidence.list_all()
        return "\n".join([f"{k}: Score={v['score']}" for k, v in conjectures.items()])

    # HPC PDE Solving
    def run_pde_solve(self, problem_type: str, size: int) -> str:
        return self.hpcsolver.solve_pde(problem_type, size)

    def run_concurrent_pde(self, tasks: List[str], sizes: List[int]) -> str:
        results = self.hpcsolver.solve_concurrent(tasks, sizes)
        return "\n".join(results)

    # Theorem Prover
    def check_theorem(self, conj_id: str) -> str:
        """
        Check the theorem associated with the given conjecture ID.
        Update confidence based on the result.
        """
        statement = self.conjecture_texts.get(conj_id, "")
        if not statement:
            return f"No text found for conjecture '{conj_id}'."

        success = self.theorem.check_proof(statement)
        if success:
            self.confidence.update_score(conj_id, +1)
            return f"Theorem check PASSED for '{conj_id}'. Confidence increased to {self.confidence.get_score(conj_id)}."
        else:
            self.confidence.update_score(conj_id, -1)
            return f"Theorem check FAILED for '{conj_id}'. Confidence decreased to {self.confidence.get_score(conj_id)}."

    # Symbolic Solver
    def symbolic_solve(self, equation: str, variables: str) -> str:
        return symbolic_solve_equation(equation, variables)

    # ML Training and Chat
    def train_csv(self, file_obj) -> str:
        return self.ml_model.train_from_csv(file_obj)

    def chat(self, user_message: str) -> str:
        return self.ml_model.chat_response(user_message)

########################################
# 8. Gradio Interface
########################################

# Initialize the pipeline
pipeline = HybridAIPipeline()

# Define Gradio functions
def add_domain_func(domain_name, key, val):
    if not domain_name.strip() or not key.strip():
        return "Domain Name and Key are required."
    return pipeline.add_domain(domain_name.strip(), key.strip(), val.strip())

def list_domains_func():
    return pipeline.list_domains()

def add_conjecture_func(conj_id, score, text):
    if not conj_id.strip():
        return "Conjecture ID is required."
    try:
        score_int = int(score)
        if not (0 <= score_int <= 5):
            return "Score must be between 0 and 5."
    except:
        return "Invalid score."
    return pipeline.add_conjecture(conj_id.strip(), score_int, text.strip())

def list_conjectures_func():
    return pipeline.list_conjectures()

def train_csv_func(file):
    if file is None:
        return "Please upload a CSV file."
    return pipeline.train_csv(file)

def chat_func(message):
    if not message.strip():
        return "Please enter a message."
    return pipeline.chat(message.strip())

def pde_solve_func(problem, size):
    return pipeline.run_pde_solve(problem, size)

def pde_concurrent_func(tasks_str, sizes_str):
    tasks = [t.strip() for t in tasks_str.split(",") if t.strip()]
    sizes = [s.strip() for s in sizes_str.split(",") if s.strip()]
    if len(tasks) != len(sizes):
        return "Number of tasks and sizes must match."
    try:
        sizes_int = [int(s) for s in sizes]
    except:
        return "Sizes must be integers."
    return pipeline.run_concurrent_pde(tasks, sizes_int)

def theorem_check_func(conj_id):
    if not conj_id.strip():
        return "Conjecture ID is required."
    return pipeline.check_theorem(conj_id.strip())

def symbolic_solve_func(equation, variables):
    if not equation.strip() or not variables.strip():
        return "Equation and variables are required."
    return pipeline.symbolic_solve(equation.strip(), variables.strip())

# Build Gradio Interface
def build_interface():
    with gr.Blocks() as demo:
        gr.Markdown("# Enterprise-Grade Hybrid AI App")

        with gr.Tab("Domain Assumptions"):
            gr.Markdown("**Add and manage domain-specific assumptions.**")
            with gr.Row():
                domain_name = gr.Textbox(label="Domain Name", placeholder="e.g., NavierStokes", value="NavierStokes")
                key = gr.Textbox(label="Assumption Key", placeholder="e.g., dimension", value="dimension")
                val = gr.Textbox(label="Assumption Value", placeholder="e.g., 3D", value="3D")
            add_domain = gr.Button("Add/Update Domain")
            domain_output = gr.Textbox(label="Output")
            add_domain.click(fn=add_domain_func, inputs=[domain_name, key, val], outputs=[domain_output])

            list_domains_btn = gr.Button("List All Domains")
            list_domains_out = gr.Textbox(label="Domains Data")
            list_domains_btn.click(fn=list_domains_func, outputs=[list_domains_out])

        with gr.Tab("Conjectures"):
            gr.Markdown("**Track conjectures with confidence scores and optional text for theorem checks.**")
            with gr.Row():
                conj_id = gr.Textbox(label="Conjecture ID", placeholder="e.g., C1", value="C1")
                score = gr.Slider(label="Confidence Score (0-5)", minimum=0, maximum=5, step=1, value=3)
            text = gr.Textbox(label="Conjecture Text (optional)", placeholder="Enter conjecture text here.", lines=3)
            add_conj_btn = gr.Button("Add Conjecture")
            conj_output = gr.Textbox(label="Output")
            add_conj_btn.click(fn=add_conjecture_func, inputs=[conj_id, score, text], outputs=[conj_output])

            list_conj_btn = gr.Button("List All Conjectures")
            list_conj_out = gr.Textbox(label="Conjectures Data")
            list_conj_btn.click(fn=list_conjectures_func, outputs=[list_conj_out])

        with gr.Tab("Train & Chat"):
            gr.Markdown("**Train a text classification model from a CSV file and interact via a chatbot.**")
            with gr.Row():
                file_input = gr.File(label="Upload CSV (columns: text, label)")
                train_btn = gr.Button("Train Model")
            train_output = gr.Textbox(label="Training Output")
            train_btn.click(fn=train_csv_func, inputs=[file_input], outputs=[train_output])

            with gr.Row():
                chat_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
                chat_btn = gr.Button("Chat")
            chat_output = gr.Textbox(label="Chatbot Response")
            chat_btn.click(fn=chat_func, inputs=[chat_input], outputs=[chat_output])

        with gr.Tab("HPC PDE Solvers"):
            gr.Markdown("**Simulate solving PDEs using FEniCS with concurrency support.**")
            with gr.Row():
                problem_type = gr.Dropdown(choices=["Poisson"], label="PDE Type", value="Poisson")
                mesh_size = gr.Slider(label="Mesh Size", minimum=10, maximum=100, step=10, value=32)
            solve_pde_btn = gr.Button("Solve PDE")
            pde_output = gr.Textbox(label="PDE Solve Output")
            solve_pde_btn.click(fn=pde_solve_func, inputs=[problem_type, mesh_size], outputs=[pde_output])

            gr.Markdown("**Run multiple PDE solves concurrently.**")
            with gr.Row():
                tasks_str = gr.Textbox(label="Tasks (comma-separated)", placeholder="e.g., Poisson,Poisson", value="Poisson,Poisson")
                sizes_str = gr.Textbox(label="Sizes (comma-separated)", placeholder="e.g., 32,64", value="32,64")
            concurrent_pde_btn = gr.Button("Run Concurrent PDE Solves")
            concurrent_pde_out = gr.Textbox(label="Concurrent PDE Results")
            concurrent_pde_btn.click(fn=pde_concurrent_func, inputs=[tasks_str, sizes_str], outputs=[concurrent_pde_out])

        with gr.Tab("Theorem & Symbolic Solve"):
            gr.Markdown("**Check theorems associated with conjectures and solve symbolic equations.**")
            with gr.Row():
                th_conj_id = gr.Textbox(label="Conjecture ID for Theorem Check", placeholder="e.g., C1", value="C1")
                th_btn = gr.Button("Check Theorem")
            th_output = gr.Textbox(label="Theorem Check Output")
            th_btn.click(fn=theorem_check_func, inputs=[th_conj_id], outputs=[th_output])

            gr.Markdown("**Symbolic Equation Solver using SymPy.**")
            with gr.Row():
                equation = gr.Textbox(label="Equation", placeholder="e.g., x**2 - 4", value="x**2 - 4")
                variables = gr.Textbox(label="Variables (comma-separated)", placeholder="e.g., x", value="x")
            symbolic_btn = gr.Button("Solve Symbolically")
            symbolic_output = gr.Textbox(label="Symbolic Solution")
            symbolic_btn.click(fn=symbolic_solve_func, inputs=[equation, variables], outputs=[symbolic_output])

        gr.Markdown("## 🚀 Welcome to the Enterprise-Grade Hybrid AI App!")
        gr.Markdown("This application integrates domain management, conjecture tracking, PDE solving, theorem checking, symbolic computation, and machine learning into a unified platform.")

    return demo

# Launch the Gradio app
app = build_interface()
app.launch()