app.py

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

from typing import Dict, Any, List, Union

# Minimal ML
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from sympy import symbols, Eq, solve
from sympy.parsing.sympy_parser import parse_expr

########################################
# 1. Domain Assumption + Confidence
########################################

class DomainAssumptionMatrix:
    """
    Store domain assumptions in a dictionary:
    domain_name -> {key: value}
    Potentially used for advanced logic or PDE constraints.
    """
    def __init__(self):
        self.matrix = {}

    def add_domain(self, domain_name: str, assumptions: Dict[str, Any]):
        if domain_name not in self.matrix:
            self.matrix[domain_name] = {}
        for k, v in assumptions.items():
            self.matrix[domain_name][k] = v

    def check_conflict(self, domain1: str, domain2: str) -> bool:
        """
        Check if domain1 and domain2 have conflicting assumptions.
        E.g. if dimension is '2D' in domain1 but '3D' in domain2, conflict = True
        """
        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, Any]:
        return self.matrix


class ConfidenceIndex:
    """
    Track conjectures and confidence scores (0-5).
    Allows updates as new evidence or checks come in.
    """
    def __init__(self):
        self.index = {}

    def add_conjecture(self, conj_id: str, score: int):
        score_clamped = max(0, min(score, 5))
        self.index[conj_id] = {
            "score": score_clamped
        }

    def get_score(self, conj_id: str) -> int:
        return self.index.get(conj_id, {}).get("score", 0)

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


########################################
# 2. PDE / HPC Stub with Concurrency
########################################

class HPCSolver:
    """
    Simulates HPC PDE solves (like Poisson or Navier–Stokes).
    We'll do concurrency to show enterprise readiness.
    """
    def solve_pde_stub(self, problem_type: str, size: int) -> str:
        """
        Simulate an HPC PDE solve by sleeping + random success output.
        'problem_type' could be 'Poisson' or 'NS' or similar.
        'size' might be a mesh dimension or something relevant.
        """
        time.sleep(0.2)  # Simulate HPC work
        return f"[{problem_type} PDE] completed on size={size} (stub)."

    def solve_in_parallel(self, tasks: List[str], sizes: List[int]) -> List[str]:
        results = []
        def worker(task, sz):
            return self.solve_pde_stub(task, sz)

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


########################################
# 3. Theorem Prover Stub
########################################

class ExternalTheoremProver:
    """
    Stub for partial verification.
    """
    def check_proof(self, statement: str, proof_idea: str) -> bool:
        # 70% chance success
        return random.random() > 0.3


########################################
# 4. The Pipeline: File-based ML, Chat, PDE, Theorem
########################################

class HybridAIPipeline:
    """
    Comprehensive pipeline for:
      - Domain assumptions
      - Confidence index
      - CSV-based text classification
      - Chat
      - HPC PDE concurrency
      - Theorem checking
    """

    def __init__(self):
        self.domains = DomainAssumptionMatrix()
        self.conf = ConfidenceIndex()
        self.vectorizer = None
        self.model = None
        self.trained = False
        self.hpcsolver = HPCSolver()
        self.theorem_prover = ExternalTheoremProver()

        # Store conjecture text, domains, etc.
        self.conjectures = {}

    # Domain
    def add_domain_assumption(self, domain_name: str, key: str, val: str):
        self.domains.add_domain(domain_name, {key: val})
        return f"Domain '{domain_name}' updated: {key}={val}"

    def view_domains(self):
        dm = self.domains.list_domains()
        return dm

    # Conjectures
    def add_conjecture(self, conj_id: str, init_score: int, text: str = ""):
        self.conf.add_conjecture(conj_id, init_score)
        if text:
            self.conjectures[conj_id] = text
        return f"Conjecture '{conj_id}' added with score {init_score}."

    def view_conjectures(self):
        listing = self.conf.list_all()
        return listing

    # CSV training
    def train_from_csv(self, file_obj) -> str:
        if file_obj is None:
            return "No file uploaded."
        try:
            df = pd.read_csv(file_obj)
            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()

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

            self.model = RandomForestClassifier()
            self.model.fit(X, y)
            self.trained = True

            return f"Trained on {len(texts)} samples. Classes = {set(labels)}"
        except Exception as e:
            return f"Error: {e}"

    # Chat
    def chat(self, user_input: str) -> str:
        """
        If model is trained, do classification. Otherwise, a fallback.
        Possibly incorporate domain assumptions or confidence logic.
        """
        if not self.trained or self.model is None or self.vectorizer is None:
            return "Model not trained. Please upload a CSV in 'Train Model' tab."

        # Classify user_input
        Xq = self.vectorizer.transform([user_input])
        pred = self.model.predict(Xq)[0]
        # For demonstration, respond with predicted label
        return f"[ChatBot] Based on your text, I'm predicting label: {pred}"

    # HPC PDE
    def run_pde_solve(self, problem_type: str, mesh_size: int):
        return self.hpcsolver.solve_pde_stub(problem_type, mesh_size)

    def run_pde_concurrent(self, tasks: List[str], sizes: List[int]) -> List[str]:
        return self.hpcsolver.solve_in_parallel(tasks, sizes)

    # Theorem Prover
    def check_theorem(self, conj_id: str):
        """
        Stub: If conj_id was stored with a text, we attempt partial proof.
        """
        statement = self.conjectures.get(conj_id, None)
        if not statement:
            return f"No statement stored for {conj_id}."

        success = self.theorem_prover.check_proof(statement, "Sketch of proof.")
        if success:
            self.conf.update_score(conj_id, +1)
            return f"Theorem check passed! Score for '{conj_id}' raised."
        else:
            self.conf.update_score(conj_id, -1)
            return f"Theorem check failed for '{conj_id}'. Score lowered."

    # Symbolic Solve
    def symbolic_solve_equation(self, equation: str, variables: str):
        var_list = [v.strip() for v in variables.split(",") if v.strip()]
        try:
            syms = [parse_expr(v) for v in var_list]
            expr = parse_expr(equation)
            eq = Eq(expr, 0)
            sol = solve(eq, syms, dict=True)
            return f"Symbolic solution: {sol}"
        except Exception as e:
            return f"Error solving symbolically: {e}"

########################################
# GRADIO
########################################

pipeline = HybridAIPipeline()

def add_domain_fn(domain_name, assumption_key, assumption_value):
    return pipeline.add_domain_assumption(domain_name, assumption_key, assumption_value)

def list_domains_fn():
    dm = pipeline.view_domains()
    return str(dm)

def add_conjecture_fn(conj_id, init_score, text):
    return pipeline.add_conjecture(conj_id, int(init_score), text)

def list_conjectures_fn():
    c = pipeline.view_conjectures()
    return str(c)

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

def chat_fn(message):
    return pipeline.chat(message)

def hpc_solve_fn(problem_type, mesh_size):
    return pipeline.run_pde_solve(problem_type, int(mesh_size))

def theorem_check_fn(conj_id):
    return pipeline.check_theorem(conj_id)

def symbolic_solve_fn(equation, variables):
    return pipeline.symbolic_solve_equation(equation, variables)

def concurrency_demo_fn(tasks_str, sizes_str):
    """
    tasks_str: comma-separated PDE tasks
    sizes_str: comma-separated mesh sizes
    """
    tasks = [t.strip() for t in tasks_str.split(",") if t.strip()]
    sizes_raw = [s.strip() for s in sizes_str.split(",") if s.strip()]
    if len(tasks) != len(sizes_raw):
        return "Error: tasks and sizes mismatch."

    sizes = [int(x) for x in sizes_raw]
    results = pipeline.run_pde_concurrent(tasks, sizes)
    return "\n".join(results)

# Build the Gradio UI

import gradio as gr

def build_app():
    with gr.Blocks() as demo:
        gr.Markdown("# Enterprise-grade Hybrid AI App")

        with gr.Tab("Domain Assumptions"):
            gr.Markdown("Store domain constraints (e.g., PDE dimension).")
            domain_in = gr.Textbox(label="Domain Name", value="FluidPDE")
            key_in = gr.Textbox(label="Key", value="dimension")
            val_in = gr.Textbox(label="Value", value="3D")
            domain_btn = gr.Button("Add Domain")
            domain_out = gr.Textbox(label="Output")
            domain_btn.click(fn=add_domain_fn, inputs=[domain_in, key_in, val_in], outputs=[domain_out])

            list_dom_btn = gr.Button("List Domains")
            list_dom_out = gr.Textbox(label="All Domains")

            list_dom_btn.click(fn=list_domains_fn, outputs=list_dom_out)

        with gr.Tab("Conjectures"):
            gr.Markdown("Track conjectures with confidence scores (0-5). Optionally store text for theorem checks.")
            conj_id_in = gr.Textbox(label="Conjecture ID", value="C1")
            conj_score_in = gr.Slider(label="Init Score", minimum=0, maximum=5, step=1, value=3)
            conj_text_in = gr.Textbox(label="Conjecture Text", value="Navier-Stokes globally well-posed.")
            conj_btn = gr.Button("Add Conjecture")
            conj_out = gr.Textbox(label="Conjecture Output")

            conj_btn.click(fn=add_conjecture_fn, inputs=[conj_id_in, conj_score_in, conj_text_in], outputs=[conj_out])

            conj_list_btn = gr.Button("List Conjectures")
            conj_list_out = gr.Textbox(label="Conjecture Listing")

            conj_list_btn.click(fn=list_conjectures_fn, outputs=[conj_list_out])

        with gr.Tab("Train & Chat"):
            gr.Markdown("**Upload CSV** with 'text' and 'label' columns to train an ML model, then chat.")
            file_in = gr.File(label="CSV File")
            train_btn = gr.Button("Train Model")
            train_out = gr.Textbox(label="Training Log")

            train_btn.click(fn=train_csv_fn, inputs=[file_in], outputs=[train_out])

            chat_in = gr.Textbox(label="Chat Input", value="Hello, model!")
            chat_btn = gr.Button("Chat")
            chat_out = gr.Textbox(label="Chat Response")

            chat_btn.click(fn=chat_fn, inputs=[chat_in], outputs=[chat_out])

        with gr.Tab("HPC PDE"):
            gr.Markdown("Simulate HPC PDE solves.")
            prob_in = gr.Dropdown(label="Problem Type", choices=["Poisson", "NavierStokes"], value="Poisson")
            size_in = gr.Slider(label="Mesh Size / Complexity", minimum=10, maximum=100, step=5, value=30)
            hpc_btn = gr.Button("Run HPC Solve")
            hpc_out = gr.Textbox(label="HPC Output")

            hpc_btn.click(fn=hpc_solve_fn, inputs=[prob_in, size_in], outputs=[hpc_out])

            # concurrency
            tasks_str = gr.Textbox(label="Tasks Comma-Separated (Poisson, NavierStokes, ...)", value="Poisson, NavierStokes")
            sizes_str = gr.Textbox(label="Sizes Comma-Separated (30,40,...)", value="30,40")
            conc_btn = gr.Button("Concurrency Demo")
            conc_out = gr.Textbox(label="Concurrent PDE Results")

            conc_btn.click(fn=concurrency_demo_fn, inputs=[tasks_str, sizes_str], outputs=[conc_out])

        with gr.Tab("Theorem Check & Symbolic Solve"):
            gr.Markdown("Stub for theorem checks & symbolic solving.")
            th_in = gr.Textbox(label="Conjecture ID for Theorem Check", value="C1")
            th_btn = gr.Button("Check Theorem")
            th_out = gr.Textbox(label="Theorem Output")
            th_btn.click(fn=theorem_check_fn, inputs=[th_in], outputs=[th_out])

            eq_in = gr.Textbox(label="Equation (e.g. 'x**2 - 4')", value="x**2 - 4")
            vars_in = gr.Textbox(label="Variables (comma) e.g. 'x'", value="x")
            eq_btn = gr.Button("Symbolic Solve")
            eq_out = gr.Textbox(label="Symbolic Output")

            eq_btn.click(fn=symbolic_solve_fn, inputs=[eq_in, vars_in], outputs=[eq_out])

        gr.Markdown("## Done: This is our 'enterprise-grade' hybrid AI app. Enjoy!")
    return demo


def main():
    demo = build_app()
    demo.launch()

if __name__ == "__main__":
    main()