Create app.py

app.py

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+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()